# Section: Resources --- title: "Enterprise AI Data Maturity Model" description: "Assess AI readiness and strengthen the data foundation for trusted AI and agents" url: "https://www.getdbt.com/resources/enterprise-ai-data-maturity-model" date: "2026-08-26" categories: ["Guide"] --- # Enterprise AI Data Maturity Model Assess AI readiness and strengthen the data foundation for trusted AI and agents ## Executive summary AI has moved beyond experimentation. Organizations are investing in AI and embedding it into everyday operations, yet true AI maturity remains rare. Over the next three years, [92% of companies](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work) plan to increase their AI investments, but only 1% of leaders consider their organizations mature in AI deployment. As AI moves into production, organizations need more than models and use cases. They need the trusted data, governance, context, and operational readiness required to scale AI with confidence. The **Enterprise AI Data Maturity Model** helps organizations assess the data capabilities required to scale AI. It outlines how organizations progress from trusted data to trusted AI and agents, helping leaders identify their current maturity stage, recognize operational gaps, and prioritize what to build next. This guide explores the five stages of that progression and provides a practical framework for evaluating AI readiness. ### **How this guide fits into the broader Fivetran + dbt Labs AI journey** This guide builds on [The data leader's primer for agentic AI](https://www.getdbt.com/resources/the-data-leaders-primer-for-agentic-ai), which explores why trusted data, governance, and context are essential for successful AI initiatives. While the primer explains why AI readiness matters, this guide helps organizations determine where they are today and what capabilities they need to progress toward trusted AI and agents. > "AI and agents are only as strong as the data behind them. By investing in Fivetran and dbt, we've built the reusable, trusted data assets that are central to how we scale AI and drive innovation." — Piyush Bhargava, Sr. Director Global Data & Analytics, DocuSign ## Why AI maturity matters now ### **AI is scaling faster than organizations can support it** Organizations are no longer asking whether AI can deliver value. They are focused on how to deploy it reliably, responsibly, and at scale. According to the [dbt Labs 2026 State of Analytics Engineering Report](https://www.getdbt.com/resources/state-of-analytics-engineering-2026), AI is becoming an integral part of analytics engineering workflows. But as AI accelerates the volume and pace of analytics work, the trust and governance mechanisms supporting that work are struggling to keep up. This creates a growing operational challenge: organizations need to move faster while maintaining confidence in the data, definitions, context, and outputs their AI systems rely on. ### **What starts to break as AI scales** As AI expands across teams and business functions, existing weaknesses become more visible. Data quality issues spread further, governance struggles to keep pace, and ownership becomes harder to define. Infrastructure and compute costs become more difficult to manage, while AI-generated answers require greater traceability and explanation. These issues share the same underlying cause: AI is scaling faster than the trusted data foundation required to support it. ### **Common barriers to AI maturity** While every organization’s journey is different, many encounter similar barriers: - Inconsistent definitions - Fragmented data governance - Poor data quality - Limited visibility into data lineage - Unclear ownership - Missing business context ### **By the numbers** - 83% say increasing trust in data and data teams is an important priority - 71% say shipping data products faster is a priority - 71% are concerned about hallucinated or incorrect data reaching stakeholders - 53% report poor data quality as a top challenge - 41% cite ambiguous data ownership as an ongoing challenge _Source: [dbt Labs 2026 State of Analytics Engineering Report](https://www.getdbt.com/resources/state-of-analytics-engineering-2026)_ ## From AI adoption to AI maturity Adopting AI is only the beginning. Maturity depends on whether an organization can support AI reliably across teams, use cases, and systems. Trusted data, governance, context, and interoperability allow organizations to move beyond isolated initiatives and build AI systems and agents that can be managed, governed, and scaled over time. ### AI maturity starts with trusted data Every AI system depends on the quality, consistency, and context of the data it receives. Without trusted inputs, shared business definitions, and governance controls, even advanced models struggle to produce reliable business outcomes. Building trusted data, along with the ownership, governance, and business context behind it, creates the foundation for trusted AI and agents. - Only about [one-third of organizations ](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work)report scaling AI across the enterprise. - Gartner identifies [data availability and quality](https://www.gartner.com/en/newsroom/press-releases/2025-06-30-gartner-survey-finds-forty-five-percent-of-organizations-with-high-artificial-intelligence-maturity-keep-artificial-intelligence-projects-operational-for-at-least-three-years) among the top challenges in AI implementation. - MIT research found that [95% of enterprise GenAI implementations](https://acrobat.adobe.com/id/urn:aaid:sc:US:38d5e625-2a9e-4e62-9406-a36716909bb9) showed no measurable P&L impact, citing challenges around workflow integration and contextual learning. - [77% of technology leaders](https://www.ibm.com/thought-leadership/institute-business-value/en-us/c-suite-study/cxo) say current AI governance frameworks are inadequate. ### Building AI maturity over time Organizations build AI maturity progressively by strengthening trust, governance, context, interoperability, and operational readiness. The **Enterprise AI Data Maturity Model** organizes this progression into two phases and five stages, helping organizations understand their current capabilities and determine what they need to develop next. ## Introducing the Enterprise AI Data Maturity Model Most AI maturity discussions focus on AI adoption. They assess AI strategy, workforce readiness, use cases, or overall adoption. While these are important indicators of AI progress, they do not fully reveal whether an organization has the trusted data infrastructure needed to support AI reliably at scale. **The Enterprise AI Data Maturity Model focuses on the trusted data infrastructure organizations need to build and scale trusted AI and agents. **Rather than measuring AI adoption alone, the model evaluates the capabilities that make it possible: - Data quality - Governance - Ownership - Cost and operational efficiency - Context - Data architecture and interoperability **** ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/5b3aaea0368b6f61036d4e563e6b1e181466a430-2157x1979.jpg) The model helps organizations assess their current AI data maturity and identify the capabilities needed to progress from trusted data to trusted AI and agents. Organizations can use it to: - Identify their current maturity stage - Understand capability gaps - Prioritize future investments - Build toward trusted AI systems and agents ### Understanding the five stages of maturity The five stages show how organizations progress from fragmented data practices to the trusted, governed, and interoperable foundation required for AI systems and agents. Each stage builds on the capabilities established in the one before it. ## Stage 0: Fragmented and reactive **Core question: Can anyone trust the data?** Data trust is low. Data is fragmented and lives within silos. Logic and definitions are scattered, ungoverned, and undocumented. Costs are difficult to understand. Organizations at this stage rely on data spread across multiple tools, with no centralization and little consistency in how it is transformed, documented, and governed. Definitions vary across teams, governance is ad hoc, and institutional knowledge often lives with individuals rather than being documented and shared. As a result, organizations struggle to scale analytics or establish the trusted data foundation needed to support AI initiatives. **Characteristics** - Data and logic scattered across tools and spreadsheets - No version control - No automated testing - No single source of truth - Limited governance - Siloed data architecture - Knowledge trapped in people’s heads **Business impact** - Minimal trust in data - Slow decision-making - High operational overhead - Limited visibility and control over data and compute costs - Reactive operating model **Common symptoms** - Multiple versions of the same metric - Frequent reporting discrepancies - Data quality issues discovered by business users - AI initiatives struggle due to inconsistent data **Priorities for progressing to Stage 1** Organizations progress by introducing consistent, repeatable data practices that improve trust and reduce operational complexity. - Establish version-controlled transformation workflows - Introduce testing and quality controls - Create trusted datasets - Begin documenting business logic ## Stage 1: Standardized and reliable **Core question: Can a team trust the data?** Data is reliable in places. Transformation logic is version-controlled, tested, and consistent. Costs are now visible. Organizations at this stage begin establishing consistent data practices within individual teams and critical domains. Some data from key sources is centralized, cleaned, and transformed into analytics-ready tables in the warehouse. Testing becomes part of the development process, while documentation begins capturing institutional knowledge that previously lived with individuals. As trust grows within these early domains, the foundation for broader governance and standardization begins to take shape. **Characteristics** - Version-controlled transformation logic - Automated testing - CI/CD processes - First trusted domains - Basic documentation - Cost visibility - Emerging analytics engineering practices - Centralization of data in a single source of truth **Business value** - Improved operational efficiency - Improved developer productivity - Better data quality - Better collaboration **What organizations gain** - Greater confidence in key datasets - Faster onboarding - More consistent reporting - Reduced manual work **Priorities for progressing to Stage 2** The next step is extending trusted practices beyond individual teams, making governance, ownership, and quality standards consistent across the organization. - Expand trusted practices beyond individual teams - Establish governance standards - Increase observability and ownership - Scale documentation and testing - Gain access to all data sources ### **How Fivetran + dbt Labs accelerate this stage** **dbt Wizard** dbt Wizard helps teams accelerate AI-assisted analytics and development while grounding AI-assisted workflows in trusted, governed data, standardized business definitions, and established organizational practices. By working from trusted context, teams can increase productivity while maintaining confidence in AI-assisted outputs. Other ways Fivetran + dbt Labs accelerate this stage: - dbt Core or dbt platform - Fivetran SaaS Connectors **** > "Before dbt Wizard, our engineers were spending more time correcting AI output than they were writing models. Now the agent actually knows our project. It gets the joins right, it respects our contracts, and it doesn't break things downstream. We've seen a 15–20% reduction in production incidents since we rolled it out." — Erion Krasniqi, Junior Data Scientist, Endress+Hauser InfoServ ## Stage 2: Scaled and governed **Core question: Can everyone trust the data?** Data is trustworthy at scale. Standardized practices are deeply adopted across teams and use cases. Governance becomes an organizational capability rather than a team-level practice. Organizations at this stage have expanded trusted data practices beyond individual teams. Data from all sources, including SaaS applications, core databases, and proprietary systems, is centralized, governed, and transformed. Clean, quality data is increasingly piped back to operational systems that need it to inform business actions and decisions. As organizations scale across teams, data mesh architectures help balance centralized governance with domain ownership, enabling trusted data to be managed closer to the business. Governance, documentation, and data quality standards, often grounded in software engineering practices such as version control, testing, and CI/CD, are applied consistently across the organization. This creates a shared foundation for trusted, self-service analytics that both people and AI systems can rely on. As adoption grows, organizations gain greater visibility into the operational cost and complexity of running AI at scale. These capabilities help improve efficiency and position them to scale AI initiatives with greater confidence. **Characteristics** - Governed pipelines - Continuous quality monitoring - Business glossary and shared definitions - Access controls and lineage - Self-service data access - Domain ownership - Reduced compute costs - Data mesh **Business value** - Scaled trust and governance - Lower warehouse costs - Less tooling and maintenance - Development capacity freed for AI initiatives **What organizations gain** - Consistent definitions across the business - Greater self-service adoption - Stronger governance - Increased confidence in enterprise-wide reporting **Priorities for progressing to Stage 3** The next phase of maturity extends trust beyond governed data to the context required for reliable AI systems and agents. - Extend trust beyond datasets into context - Establish semantic consistency - Create machine-readable governance - Enable traceability for AI outputs - Increase data ownership and portability ### **How Fivetran + dbt Labs accelerate this stage** **dbt State** As trusted data practices scale across teams, understanding what has changed becomes increasingly important. dbt State helps teams identify what has changed between project states so they rebuild only what's necessary. This reduces compute costs, accelerates development, and makes governed analytics more efficient as AI initiatives grow. Other ways Fivetran + dbt Labs accelerate this stage: - dbt platform - dbt Mesh - dbt MCP server - Fivetran Database Connectors - Fivetran Connector SDK - Fivetran Activations **** > "Before dbt State, every job rebuilt every model in the lineage. Every. Single. Time. Now, with dbt State, dbt checks if source data changed. If it didn't, the model is skipped. For us, that resulted in a 9% compute reduction, 35% fewer models built, and a 15% reduction in Snowflake backfill costs." — Parag Shah, Vice President of Data, CarGurus ### Supporting research 45% of high-AI-maturity organizations keep AI initiatives operational for three years or more, compared to 20% of low-maturity organizations. _Source: [Gartner research](https://www.gartner.com/en/newsroom/press-releases/2025-06-30-gartner-survey-finds-forty-five-percent-of-organizations-with-high-artificial-intelligence-maturity-keep-artificial-intelligence-projects-operational-for-at-least-three-years)_ **** > “With Fivetran and dbt, what used to take months now happens in weeks, giving the business faster access to trusted data and creating the foundation we need to scale analytics, agents, and AI across the enterprise.” — Akshay Agrawal, Director of Data Engineering at Zendesk ## The inflection point: From trusted data to trusted AI and agents The first three stages focus on building trusted data. Stage 3 marks the shift from creating reliable data foundations to enabling trusted AI systems that can reason over governed context. This is where semantic context, governance, and lineage become critical—not just for people, but for AI. This is where trusted data begins powering trusted AI systems and agents. ## Stage 3: Trusted and contextual **Core question: Can we trust AI outputs and AI-assisted work?** Trusted data becomes trusted context that AI systems and agents can work with. Semantic definitions, lineage, and business documentation are exposed through a shared context layer in an open, machine-readable format that AI systems can consistently interpret and reason over. Governance is machine-readable and structured for AI consumption and reasoning, making outputs traceable and verifiable. At this stage, organizations land data in open formats with metadata and lineage intact, allowing AI systems and agents to reason over trusted context back to the source while maintaining consistency and interoperability across engines. Architectures are designed to remain flexible across compute platforms, LLMs, engines, and agents, allowing organizations to optimize for cost and efficiency as AI adoption grows. Governance, business definitions, and metadata are structured so AI systems can consistently interpret, reason over, and use organizational data. As a result, AI outputs become explainable, traceable, and grounded in trusted data. **Characteristics** - Semantic models and metric definitions exposed to AI - Shared context layer for agents - Machine-readable governance - Agent evaluation frameworks - Data contracts - Auditability - Traceable AI outputs - Open, standards-based foundation **Business value** - Trustworthy agent outputs - Conversational analytics adopted broadly - Faster, more informed decision-making - Reduced token costs - Greater data ownership and reduced vendor lock-in **Priorities for progressing to Stage 4 **The next phase of maturity extends governance and trust beyond individual systems, enabling autonomous AI systems and agents to operate consistently across platforms. - Extend governance beyond individual systems - Enable cross-platform interoperability - Enable governance to travel with the data - Prepare for autonomous operation at scale ### **How Fivetran + dbt Labs accelerate this stage** **dbt Semantic Layer** dbt Semantic Layer exposes trusted business definitions, metrics, and semantic context in a machine-readable format that both people and AI systems can consistently understand and use. By providing governed context across analytics and AI workflows, it helps organizations build explainable, traceable AI on trusted data. > “By centralizing our entity relationships in the dbt Semantic Layer, where all of our data transformations already live, we could easily create visualizations in our B2B product. We delivered an improved data experience for our B2B partners by eliminating a step in our process, decreasing our data costs by 80%, and increasing reliability and trust.” — Ben Kramer, Senior Director of Analytics, Bilt Rewards ### **By the numbers** - 71% of data professionals are concerned about hallucinated or incorrect data reaching stakeholders - _Source: [dbt Labs 2026 State of Analytics Engineering Report](https://www.getdbt.com/resources/state-of-analytics-engineering-2026)_ - 77% of technology leaders say current AI governance frameworks are inadequate - _Source: [IBM research](https://www.ibm.com/thought-leadership/institute-business-value/en-us/c-suite-study/cxo)_ - MIT research found that 95% of enterprise GenAI implementations showed no measurable P&L impact - _Source: [MIT research](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/)_ ## Stage 4: Unified and interoperable **Core question: Can we scale and govern autonomous agents?** Agents reason more reliably when governance and context travel with them. Context extends beyond the transformation layer to include source metadata, enabling agents to interpret and use both structured and unstructured data. Agents operate autonomously at scale across platforms while remaining governed and auditable. Organizations at this stage have established the trusted context, governance, and interoperability needed to support autonomous AI systems and agents. Governance extends across platforms and environments, allowing agents to operate with consistent business definitions while remaining grounded in trusted data. This enables organizations to scale AI with greater confidence while maintaining oversight, control, and accountability. **Characteristics** - Autonomous pipeline management - Automated trust checks - Governance that travels with the data - Cross-platform interoperability - Living institutional knowledge - Agent-native operating model **Business value** - Cross-functional agent coordination - Self-optimizing infrastructure - Risk reduction through auditable governance - Enterprise-scale AI acceleration ### **How Fivetran + dbt Labs accelerate this stage** Fivetran and dbt Labs together provide the open, interoperable data foundation needed to support autonomous AI systems at enterprise scale. By combining trusted data movement, governed transformation, and shared context, organizations can enable AI systems and agents to operate consistently across platforms while maintaining trust, traceability, and control. Capabilities featured at this stage: - Integrated workflows - Open Data Infrastructure - Cross-platform orchestration **** > “Our focus now is on how we operationalize AI across Inova. With Fivetran and dbt, we’re creating the foundation for AI agents and applications that can act on trusted, governed data — not just generate insights, but drive action.” — Jon McManus, Chief Data and AI Officer, Inova Health ## What high-maturity organizations have in common Organizations that successfully scale AI have one thing in common: they invest in the data infrastructure that makes trusted AI possible. Rather than relying on isolated AI initiatives, they build the trusted data, governance, and context that enable AI systems to operate reliably at scale. Common patterns among higher-maturity organizations: - Ownership is clearly defined - Governance is proactive rather than reactive - Definitions are standardized and accessible - Trust extends beyond data into context - AI outputs are explainable and traceable - AI systems operate within governed boundaries - Data and infrastructure are open and interoperable, avoiding vendor lock-in - Systems serve both human analysts and AI agents from the same trusted foundation - Teams spend less time validating outputs and more time creating value ### **Key takeaway** Organizations that successfully scale AI do not simply adopt more AI tools. They build the trusted data, governance, and context that allow AI systems and agents to operate reliably. ## Assess your AI data maturity Most organizations exhibit characteristics from multiple stages simultaneously. AI maturity should be viewed as a progression rather than a binary state. An honest assessment provides the greatest value. Identify the stage that best reflects your organization's current capabilities, then use the results to identify capability gaps, prioritize investments, and determine where to focus next. ### Maturity dimensions - **Data quality:** How reliable, accurate, and consistent is your data? - **Governance:** How consistently are standards, policies, and controls applied? - **Ownership:** Are data assets clearly owned and accountable? - **Cost and operational efficiency:** How effectively does your organization manage data and AI infrastructure costs while scaling? - **Context:** Can AI systems access trusted business definitions, lineage, metadata, and semantic meaning? - **Data architecture and interoperability:** How well does your data architecture support interoperability, portability, and AI at scale? ### How to use the assessment Review each maturity dimension and identify the description that best reflects your organization's current state. Most organizations will recognize characteristics from multiple stages. Identify where most responses align, while noting any dimensions that lag behind or are significantly more advanced. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/c1162a6be21bfe741f7d364bab45b17968a30008-2430x2508.jpg) ### Interpreting your results **Mostly stage 0** Focus on building trusted data foundations through standardized development practices, testing, documentation, and governance. **Mostly stage 1** Expand trusted practices beyond individual teams by establishing shared governance, ownership, and quality standards. **Mostly stage 2** Extend trusted data into trusted context by introducing semantic consistency, machine-readable governance, and AI-ready capabilities. **Mostly stage 3** Prepare for autonomous AI by enabling interoperability, governance that travels with the data, and cross-platform operations. **Mostly stage 4** Continue optimizing autonomous AI systems while strengthening governance, interoperability, and organizational oversight.  Most organizations will find their capabilities span multiple stages. Rather than focusing on isolated strengths or weaknesses, identify the stage that best reflects your overall operating model and prioritize the capabilities that will have the greatest impact on your progression to the next stage. ## Next steps Your assessment is a starting point, not an endpoint. Use the results to align stakeholders around your organization's current capabilities and define the next phase of your AI maturity journey. - Align stakeholders around current capabilities, gaps, and priorities - Identify the capabilities required to reach the next stage - Build a roadmap for trusted AI systems and autonomous agents - Explore how dbt products and capabilities can help accelerate your progression through each stage of maturity - Review your AI readiness with a dbt expert to validate priorities and define your next steps - Revisit the assessment periodically as your AI initiatives mature ## The path to trusted AI and agents The ability to scale AI with trust, governance, and control is becoming a defining competitive advantage. The path to trusted AI and agents isn't about preparing for a single technology or model. It's about building the foundation that enables organizations to scale AI with confidence as it continues to evolve. Ultimately, it's a path to organizational resilience. The models will change. The tools will evolve. Organizations that invest in these capabilities today will be best positioned to innovate, compete, and lead — ready for the next generation of AI systems and agents and the opportunities they'll create. ### Key takeaways - Assess your organization's current AI maturity - Identify the capabilities needed to reach the next stage - Build the trusted data, governance, and context required to scale AI with confidence - [**Review your AI readiness with a dbt expert**](https://www.getdbt.com/contact) *** --- --- title: "dbt State: build what's changed, skip what hasn't" description: "dbt State optimizes every model run by building only what's changed — reducing warehouse costs and speeding up development." url: "https://www.getdbt.com/resources/dbt-state-resource" date: "2026-06-30" categories: ["Data Sheet"] --- # dbt State: build what's changed, skip what hasn't dbt State optimizes every model run by building only what's changed — reducing warehouse costs and speeding up development. dbt State brings intelligent state awareness to every model run. By checking warehouse metadata and model SQL before executing, it automatically decides whether to build, skip, clone, or defer each model — so you're only running what actually needs to run. The result: lower warehouse compute costs, faster development cycles, and no brittle manual workarounds. Works out of the box with dbt Core and the dbt platform. --- --- title: "The data infrastructure for agents you can trust" description: "Fivetran + dbt Labs deliver the fresh, governed data infrastructure AI agents need to move from experimentation to production." url: "https://www.getdbt.com/resources/fivetran-dbt-labs-merger-overview" date: "2026-06-23" categories: ["Data Sheet"] --- # The data infrastructure for agents you can trust Fivetran + dbt Labs deliver the fresh, governed data infrastructure AI agents need to move from experimentation to production. AI agents are becoming the primary consumers of data — but most infrastructure wasn't built for them. The Fivetran and dbt Labs merger changes that, delivering fresh, governed, and portable data infrastructure that scales with agent demand. See how the combined platform gives your agents the reliable foundation they need to move from experimentation to production. --- --- title: "dbt Wizard: the AI agent built for analytics engineering" description: "Meet dbt Wizard — the AI agent that knows your dbt project, checks its own work, and helps you ship faster." url: "https://www.getdbt.com/resources/dbt-wizard-resource" date: "2026-06-23" categories: ["Data Sheet"] --- # dbt Wizard: the AI agent built for analytics engineering Meet dbt Wizard — the AI agent that knows your dbt project, checks its own work, and helps you ship faster. Most AI coding tools are generic. dbt Wizard is different — it's an agent with native understanding of your dbt project's lineage, tests, contracts, and metric definitions. It investigates what broke, builds validated models, refactors across every affected file, and checks its own work before surfacing anything for review. Available in dbt Studio and as a terminal-native CLI. --- --- title: "The analyst revolution: Unlocking tomorrow’s AI initiatives" description: "How modern organizations eliminate data bottlenecks to gain a competitive advantage" url: "https://www.getdbt.com/resources/the-analyst-revolution-unlocking-tomorrows-ai-initiatives" date: "2026-04-29" categories: ["Guide"] --- # The analyst revolution: Unlocking tomorrow’s AI initiatives How modern organizations eliminate data bottlenecks to gain a competitive advantage ## Introduction Analysts sit at the forefront of AI-driven decision-making. They’re responsible for transforming raw data into insights that shape strategies, direct investment, and accelerate growth. Yet despite their critical role, analysts remain trapped in outdated workflows, restricted access regimes, and fragmented tooling that prevent them from operating at their full potential. Luckily, we’re in the beginning stages of an _analyst revolution_. Research reveals how deeply organizations are losing productivity, talent, and competitive advantage by underinvesting in analysts’ workflows. At the same time, a path forward has emerged, with self-service environments that have built-in guardrails to enable analysts to move quickly without breaking governance. This report, produced by dbt Labs in partnership with The Harris Poll, draws on a survey of 510 analysts across industries. It explores the current state of analyst work, the systemic risks of inaction, and the transformational opportunities available when organizations empower analysts with AI-ready, governed platforms. The findings show how these types of self-service environments can unlock a new era of analyst impact, which makes a compelling case for C-suite leaders—empowering analysts with structure and trust is a strategic imperative. ## Methodology dbt Labs partnered with The Harris Poll to conduct a 15-minute online survey from May 28 – June 3, 2025. The survey included 510 respondents in analyst roles, including data analysts, business analysts, quantitative analysts, data specialists, and data scientists. All respondents work with organizational data at companies with 500+ employees across Finance, Healthcare/Pharma, Technology, CPG/Retail, Power & Utilities, and Industrial Manufacturing. ## Executive Summary Our research revealed a workforce caught between rising expectations and outdated systems. Key highlights include: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/08ec5e90eca972399d457a33c606dde989e750b6-1334x414.png) The evidence is decisive. Organizations that empower analysts with governed, AI-ready platforms accelerate insight velocity, reduce compliance risks, and become magnets for top talent. ## Chapter 1: The great analyst crisis ### Analysts love their work—but hate their workflows Analysts aren’t burned out because they dislike their jobs. Instead, they’re frustrated because their performance is adversely affected by inefficient workflows. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/f5508d17bd1a082d0aef20967d33dfd0eade3c90-1454x550.png) > > > — Survey respondent The result is a paradox. Although **75% report that organizational data is trustworthy** and **62% say it is well-governed**, analysts spend far more time validating data than analyzing it. Restoring analyst autonomy isn’t just good for morale. It unlocks faster, more confident decision-making across the business. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/20237f2593b67a8add3876eb5c79ab049ea6d3d1-1482x968.png) Across industries, analysts lose **9.1 hours per week** to inefficiencies. At a median salary of $95,000, this equates to a **$21,613 annual productivity loss per analyst**. For a 1,000-analyst enterprise, the hidden tax totals **$21.6 million annually**. And this is before factoring in delayed insights, slower responses, and reduced agility. It’s an unnecessary friction that creates a silent drag on business growth. Instead of acting as accelerators of decision-making, analysts are constrained by fractured workflows that dilute their impact. ## Chapter 2: The governance gap ### Tool sprawl and shadow IT / AI The adoption of self-service tools was meant to empower analysts. Instead, it created sprawl and introduced new risks. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/bd19754831bac53bc4c1141e811506facf765903-1466x536.png) While these actions may speed up short-term analysis, they undermine governance, creating inconsistent metrics, fragmented workflows, and regulatory exposure. Critically, **63% of analysts acknowledge that working outside governed systems further delays projects**, as teams must validate outputs retroactively. > > > — Survey respondent ### The C-suite disconnect Executives demand faster insights, but fail to equip analysts with the necessary tools: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/4d040d45398b836a882becdae81a1fefea133fa8-1206x450.png) The result is an environment where organizations oscillate between two extremes. Locking down access too tightly, which frustrates analysts and slows agility, or opening the floodgates, which exposes the enterprise to compliance and cost risks. Either choice leaves analysts under-empowered and executives under-served. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/2ee574708c3deacf6c378dab12c380999d06001c-1164x508.png) This disconnect becomes more obvious when we examine what analysts describe as the “efficiency paradox,” where the tasks most critical to business outcomes are also the most difficult to execute. Equipping analysts with the right tools and access helps organizations move faster, collaborate better, and act on insights when it matters most. ### The efficiency paradox Analysts face a striking imbalance between the importance of their work and the ease with which they can execute it. Survey data shows that while at least **70% rate tasks like maintaining compliance and ensuring accuracy as highly important**, only **38–40% find them easy to complete.** ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/d3285d654b8f6e23218d4bb6e50c6dcec48d2bf9-1452x494.png) This gap highlights a core inefficiency—the responsibilities most critical to business outcomes are also the most difficult to deliver. So instead of driving insights, analysts spend hours validating trusted data or waiting for access approvals. For executives, the consequences are significant. Bottlenecks in compliance and accuracy checks ripple into delayed projects, hesitant decision-making, and increased risk exposure. The frustration stems not from the importance of these tasks, but from the unnecessary barriers that make them harder than they should be. These gaps create mounting frustration, but the research also reveals where analysts see the greatest opportunity for change: the integration of AI into governed workflows. Because when analysts are supported by AI-powered tools, they shift from execution to impact, surfacing insights that guide smarter, faster business decisions. ## Chapter 3: The AI skills unlock ### Analysts want centralized AI-powered, governed workflows Across industries, AI is lessening the burden of mundane data tasks and improving the ability to focus on work that matters, so it's no surprise that analysts are eager for it to become a central part of their workflows. **In addition, there’s overwhelming demand for governed self-service, signaling the need for a central environment where analysts can safely self-serve with standardized access and metrics. **The survey results show an overwhelming appetite for an all-in-one, structured, AI-powered platform. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/c8bc44fd9a9070df136d25556d9e907504ceb9b3-1400x466.png) Analysts also have strong opinions on the AI features that will create the most impact. When asked which capabilities would be most valuable in their workflows, they prioritized: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/3c8523a8014e846bf94d8316ede5515816945d72-1152x354.png) These preferences reveal a desire for speed _and_ trust. Analysts want to spend less time validating datasets and more time guiding decisions with reliable insights. Furthermore, they’ll co-lead an AI-enabled analytics lifecycle, defining use cases, acceptance criteria, and trust signals that make agentic work safe. Their impact moves from writing queries to curating definitions, monitoring outcomes, and guiding strategy with explainable results. ### From confidence gaps to future readiness Today, analysts feel the weight of governance bottlenecks and inefficient workflows, but they also recognize the potential of AI to close those gaps. **Analysts see AI as an enabler, not a threat.** The research shows that: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/1c6e6035ec488f370cab5d28ef0ed66f0624e8cc-1454x268.png) This signals a shift in the very nature of the analyst role. Analysts are preparing to become **data strategists** rather than data wranglers, relying on AI to automate repetitive mechanics while they focus on higher-order business impact. ### The technical unlock: From code to conversation Analysts envision a future where prompting replaces manual coding. This isn’t about eliminating skill, but multiplying impact. Analysts will spend less time writing SQL and more time interpreting results, collaborating with stakeholders, and shaping organizational strategy. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/5bfaa2ebf0489d5c543a2355b3b7b058c95509a9-1470x434.png) The AI unlock, then, is not theoretical. It’s a business imperative already visible in how analysts view their work today and anticipate their role tomorrow. Organizations that fail to invest in governed, AI-powered platforms risk leaving their most strategic minds stuck in manual workflows, while competitors accelerate with speed, trust, and scale. ## Chapter 4: The multiplier effect ### Business outcomes of governed self-service Empowering analysts with governed autonomy benefits productivity, governance, and talent retention. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/d0d1feaa5d340a6af3e37f9c1b819e53ed30a4cb-1158x398.png) Governed self-service transforms analysts into data-driven decision-making accelerators. By eliminating tool fatigue and ensuring access to datasets and metrics standardized by the data team through a centrally governed self-service model, organizations increase insight velocity, improve compliance confidence, and reduce redundant work. ### The retention unlock The stakes for talent retention are high. Analysts are transparent about what keeps them engaged and what pushes them away: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/94b970c3d0ac3cb56eb6fff31931784d48567afa-1158x444.png) These numbers reveal more than employee preferences. They point to the foundations of organizational competitiveness. When workflows are outdated, analysts disengage, productivity slows, and attrition risk rises. For leaders, that means not only replacement costs, but also the loss of institutional knowledge and momentum on critical data initiatives. The survey also highlights how analysts expect the adoption of modern platforms to impact their employment. With better tooling: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/b1ea9abc44743f134e1242d5af3bde965b23a74f-1340x428.png) Together, these findings show that analysts tie modern workflows to both efficiency and to the quality and reliability of the insights they deliver. Improvements in data consistency reduce rework and conflicting metrics across teams, while greater job satisfaction strengthens retention and engagement. For executives, this means investments in workflow optimization don’t just speed up analysis. They directly improve the accuracy of decisions and the stability of the workforce delivering them. **Retention, then, becomes a leading indicator of competitive advantage: organizations that invest in governed self-service are not only equipping analysts to do better work, they are positioning themselves as talent magnets in a highly competitive labor market.** ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/6294213c1aa21d5161938cd85e609e4cd51b275d-964x618.png) ## Chapter 5: The path forward The report clearly illustrates that analysts aren’t asking for less governance or unchecked autonomy. Rather, they’re asking for a new model, one where governed self-service gives them the ability to move quickly, responsibly, and at scale, **all in one place**. This isn’t just a workflow upgrade. It’s an urgent and unavoidable shift in how organizations think about data, talent, and competitive advantage. For executives, the mandate is simple. **Empower analysts to operate at their full potential.** This means removing the inefficiencies that waste millions in lost productivity, closing the governance gaps that drive risky workarounds, and investing in **centrally governed, **AI-enabled platforms that elevate analysts from data processors to strategic advisors **driving tomorrow's AI initiatives for the business.** The analyst revolution is here. Organizations that act now will improve insight velocity and data quality, while also becoming magnets for top talent. Those that delay will fall behind. The choice is stark—remain trapped in busywork, or unleash analysts as the innovators who will define the AI era. Analysts are ready to lead the next era of business transformation. The question is whether organizations will empower them with the structure they need to succeed. --- --- title: "The dbt Fusion engine: The foundation for analytics and AI" description: "dbt Core set the standard for analytics engineering. The dbt Fusion engine raises it." url: "https://www.getdbt.com/resources/the-foundation-for-analytics-and-ai-fusion-report" date: "2026-04-29" categories: ["Report"] --- # The dbt Fusion engine: The foundation for analytics and AI dbt Core set the standard for analytics engineering. The dbt Fusion engine raises it. dbt Core set the standard for analytics engineering. The dbt Fusion engine raises it. This report examines the Fusion engine from two vantage points: the development workflow you experience every day, and the strategic decisions your team faces on cost efficiency, governance, and AI readiness. Inside: - A practitioner's guide to Fusion in the IDE - State-aware orchestration and cost efficiency - Governance and AI readiness - The upgrade path from dbt Core - Results from teams already running Fusion Download the report to see what Fusion changes today—and what it unlocks over the next 12 to 24 months. --- --- title: "Self-hosting dbt vs. dbt platform" description: "Compare what you get when you move from self-hosted dbt to a fully managed dbt platform, and what it costs to stay where you are." url: "https://www.getdbt.com/resources/self-hosting-dbt-vs-dbt-platform" date: "2026-04-14" categories: ["Data Sheet"] --- # Self-hosting dbt vs. dbt platform Compare what you get when you move from self-hosted dbt to a fully managed dbt platform, and what it costs to stay where you are. Better data quality, lower costs and risks, faster AI initiatives. Compare what you get when you move from self-hosted dbt to a fully managed dbt platform, and what it costs to stay where you are. --- --- title: "2026 State of Analytics Engineering Report" description: "AI is embedded. Now comes the harder part: governance, trust, and data quality that can keep pace." url: "https://www.getdbt.com/resources/state-of-analytics-engineering-2026" date: "2026-04-10" categories: ["Report"] --- # 2026 State of Analytics Engineering Report AI is embedded. Now comes the harder part: governance, trust, and data quality that can keep pace. ## Analytics engineering, accelerated Analytics engineering has entered a new phase of maturity. Artificial intelligence is no longer experimental inside data teams. Instead, it's funded, embedded, and actively reshaping analytics engineering workflows, influencing how code is written, how insights are generated, and how teams invest in analytics and data infrastructure. What was once exploratory is now operational. In 2026, the field is defined by AI-driven acceleration and the pressure it creates. At the same time, the core challenges that have long defined analytics engineering—data quality, ownership clarity, governance discipline—remain largely unchanged. AI is expanding what analytics teams can build and deliver. But the reliability of those outputs depends on the systems that govern them: validation, clear ownership, and strong data controls. The 2026 State of Analytics Engineering Report highlights a defining dynamic: AI is scaling analytics output faster than the trust and governance mechanisms designed to support it. The central question for 2026 is whether data teams can meet growing demands for speed and productivity without compromising data quality and trust. ### Here’s what we found - AI-assisted coding is now embedded in daily workflows. - Trust and speed are emerging as the top performance priorities - Governance concerns remain elevated. - Data infrastructure costs are outpacing budget growth. ### 2025 → 2026: What changed The shift from 2025 to 2026 is less about expansion and more about consolidation. What accelerated last year is now becoming embedded in everyday analytics workflows. Three patterns define the transition: **1. Acceleration → Integration
** AI-assisted coding has moved from rapid adoption to embedded practice. What surged in 2025 is stabilizing as a baseline workflow in 2026. **2. Enablement → Reliability
** Trust has become a clear strategic priority, even as structural challenges like ambiguous ownership remain largely unchanged. The focus is shifting from expanding access to reinforcing reliability. **3. Speed vs. cost → Speed vs. trust
** Speed increased significantly year-over-year, while cost priorities saw only marginal growth. Performance expectations are no longer defined primarily by efficiency; they're increasingly defined by the ability to maintain trust and reliability while moving faster. ##### 2025 revealed potential. 2026 requires discipline. The 2026 State of Analytics Engineering Report explores how data teams are operating under **sustained acceleration.** It captures where priorities are shifting, where constraints persist, and what those signals reveal about the next phase of analytics engineering. ## Key Insights ### AI adoption is reshaping analytics engineering **** **Defining AI in this report** To ensure clarity, this report distinguishes between two forms of AI use: **AI assisted coding** LLMs used by practitioners to draft or refactor analytics code (SQL, Python, YAML), tests, and documentation. **AI-generated insights** Stakeholder-facing outputs produced from natural-language prompts. When “AI” is used in section headers, it refers broadly to both categories. In analytical sections, we specify which layer is being discussed. ### AI is embedded in analytics workflows AI is no longer exploratory inside analytics teams; it’s embedded in daily workflows. A clear majority of respondents (72%) prioritize AI-assisted coding within their development process. Among leaders, more than 77% emphasize AI for productivity gains. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/bc137c1d01a17406c99754b7947f2614db1244ab-1112x584.png) LLM usage is now embedded in analytics development workflows, primarily reducing cycle time from draft to production. AI-assisted coding has become a strategic focus, though investment remains uneven across the pipeline. While **72% prioritize AI-assisted coding, only 24% prioritize AI-assisted pipeline management,** including testing, observability, and quality controls. The signal is clear: teams are leveraging AI to accelerate creation, but not to reinforce governance at the same pace. This isn’t a distinction between coding and pipeline work. AI-assisted coding contributes direction to pipeline development. The contrast is between acceleration and stabilization: - **Acceleration: **generating models, transformations, and documentation faster. - **Stabilization: **reinforcing validation, testing, observability, and governance controls within the data pipeline AI investment is currently weighted more heavily toward acceleration. > > > — Kasey Mazza, Hubspot ### Governance pressure is rising As AI adoption expands across analytics workflows, governance readiness is becoming a critical concern. Several signals in this section point to a growing gap between acceleration and the systems designed to validate and govern AI-assisted outputs. As AI expands what analytics teams can produce, concern is rising alongside adoption. This raises a central governance question for 2026: whether validation, testing, and oversight mechanisms are scaling at the same pace as AI-driven output. > > > — Bruno Lima, phData ### Risk awareness remains elevated Adoption has increased, and so has awareness of the risks that accompany it. A significant majority of respondents (71%) are concerned about hallucinated or incorrect data reaching stakeholders. However, the intensity of concern varies across roles. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/6ed1440bad8915dee2a8be22e07ed0c3fe8d78a2-1112x876.png) Practitioners report higher levels of concern around implementation risks. They show a 7-percentage-point higher level of concern than leaders about exposing sensitive data to LLMs. They also report elevated concern around the quality implications of AI-assisted development. By contrast, leaders are more likely to frame challenges in terms of compliance, documentation, and governance preparedness. The pattern suggests proximity matters. While practitioners experience execution risk directly, leaders experience governance responsibility structurally. AI adoption is accelerating across two fronts: engineering throughput driven by AI-assisted coding, and stakeholder-facing outputs through AI-generated insights. However, investment in validation, testing, and governance mechanisms isn’t scaling at the same rate. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/ea89e37d5b7afc307b779a91432ee82082969e34-1112x798.png) The imbalance is clear. Key indicators of AI acceleration, such as prioritization of AI-assisted coding, sit in the 70-80% range, with trust carrying similar strategic-weight (83%). Yet governance investment and foundational data constraints remain uneven. **Output is scaling faster than stabilization. ** > > > — James Waller, Lendable #### What this means LLM usage across analytics workflows has moved from experimentation to implementation. Its primary impact today is operational—helping teams ship code and analysis faster by reducing cycle time from draft to production. AI is scaling across two dimensions: increasing engineering throughput and expanding stakeholder-facing outputs. However, investment in validation, testing, and governance mechanisms isn’t scaling at the same rate. As delivery speeds increase and AI-generated insights reach broader audiences, the controls designed to safeguard that output are maturing more slowly. **Acceleration without stabilization compounds risk. ** > > > — Bruno Lima, phData ![Join our live 2026 State of Analytics Engineering virtual event — April 29 - 30, 2026](https://cdn.sanity.io/images/wl0ndo6t/main/06b6d11bb39e067d8879131b48d72cc7b0a6783e-1200x760.png) ## Trust and speed are emerging as the top performance priorities Performance expectations for analytics teams are shifting. In 2026, trust and speed are the most emphasized priorities, even as cost pressures remain present. The share of respondents who say increasing trust in data and data teams is important rose from 66% in 2025 to 83% in 2026. The importance placed on speed also climbed sharply, from 50% to 71%. Cost reduction, by comparison, saw only modest movement, edging up from 48% to 53%. The importance placed on trust and speed is increasing more rapidly than any other objective measured, signaling a shift in how analytics performance is defined: not just output, but reliable output delivered quickly. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/22c23e541c23e04ecbb8fdbc4fe2013e05a475a7-1112x584.png) The combined concentration of “Important” and “Very Important” responses in 2026 reinforces this shift. Trust now carries the highest overall prioritization across objectives, and its year-over-year increase is now more pronounced than speed or cost. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/be74a4b4d1533e9cd32a663e569d075e958aaf8b-1112x584.png) Analytics teams have historically been evaluated heavily on output volume and efficiency. In 2026, that’s no longer sufficient. As AI-assisted coding and AI-generated insights increase the speed and scale of delivery, the expectation isn’t simply faster insights; it’s faster insights that stakeholders can depend on. Cost discipline remains relevant, but it’s no longer the primary performance priority. Velocity alone is insufficient; organizations expect faster delivery paired with greater confidence in the results. In this context, maturity is defined by reliability under acceleration—the ability to move quickly while maintaining accuracy, consistency, and trust. #### What this means Speed and cost remain important, but trust now anchors both. As AI-generated insights increasingly reach stakeholders, reliability becomes part of how analytics performance is judged. Organizations are no longer optimizing primarily for output or efficiency; they’re expected to deliver insights that stakeholders can depend on at increasing speed. > > > — Pooja Crahen, Okta ## Integration has improved; trust constraints persist Not all challenges in analytics engineering are moving in the same direction. Some operational friction is easing, but structural constraints remain. The share of respondents citing “integrating data from various sources” as a top challenge declined from **35% in 2025 to 27% in 2026.** Technical integration, once a defining pain point, appears to be becoming more manageable for many teams. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/c4522b0f125909cd046e0eb40a3b7ef1f8d2b801-1112x584.png) Meanwhile, other signals have remained remarkably consistent. Ambiguous data ownership remains a challenge for 41% of respondents, effectively unchanged year over year. Poor data quality continues to be the most frequently reported obstacle across organizations. Data literacy among stakeholders remains a barrier for 36% of respondents, down slightly from 39% last year, reinforcing that trust gaps extend beyond technical systems into organizational dynamics. Integration challenges are declining, but ownership, quality, and literacy constraints persist, shifting the bottleneck from infrastructure to accountability. Perceptions of these challenges also vary by role. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/497ffb58e65d0f521cb4c05e9016cd922edf62a0-1112x930.png) While most challenge perceptions are broadly aligned across roles, differences emerge in select areas, reflecting variations in day-to-day responsibility and oversight. Notably, fewer teams now cite “lack of trust in data from stakeholders” as a primary challenge (33% to 24% YoY), even as trust rises sharply as a strategic priority. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/72a7bf5aac49357cbb9b18b169f850ecc4ce8a6a-1112x584.png) #### What this means Improvements in technical integration haven’t eliminated deeper trust constraints. While fewer teams describe trust as an acute operational issue, more now frame it as a strategic expectation, signaling a shift from isolated friction to structural accountability. The work of maintaining quality, clarifying ownership, and aligning stakeholders hasn’t disappeared; it’s become foundational. As acceleration increases and trust expectations rise, unresolved quality, ownership, and literacy gaps carry greater consequence. Analytics engineering is evolving from an enablement layer to a control layer, responsible for ensuring that intelligent systems scale reliably, not just rapidly. > > > — Jeremy Chia, Soap Cycling Singapore ## Budgets are growing—data infrastructure costs are growing faster Investment in analytics engineering continues, but growth is uneven. In 2026, **36% of respondents report increased team budgets, **while 28% report no change, 14% report decreases, and 19% are unsure of their team’s budget status. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/b574bdb73ef11a1fc33e8a515714de48164bbe49-1112x584.png) At the same time, spending on analytics and AI infrastructure—such as data warehouses, compute resources, and data management software—is rising more broadly. A majority of respondents (57%) report increased warehouse and compute spend, while only 13% report decreases. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/1635a3737dac0fbd1424a46a7657fefa21cdcec2-1112x584.png) Budget growth and infrastructure growth aren’t moving in lockstep. More teams report increased warehouse and compute spend than report budget expansion. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/d0caf105dc43dd28518d18ca9ab11eced65fc3d8-1112x546.png) The gap becomes more visible when comparing the full distribution of responses across the past 12 months. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/b18bff801c7930b16a3766421f3aca24e260ce36-1112x764.png) While both budgets and infrastructure spend have increased for many teams, warehouse and compute spend is more concentrated in higher growth bands, reinforcing the uneven pace of expansion. When infrastructure costs rise faster than overall budgets, teams operate under sustained financial pressure. As workload intensity and data consumption expand, compute demand grows alongside them. Cost optimization, therefore, isn’t a signal of contraction; it’s a necessary response to sustained scale. ### Future investment priorities Leaders are more likely to prioritize increased investment in data tooling over the next 12 months, reinforcing their focus on long-term capability building alongside budget expansion. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/b63db40ed4083afdd049f6e87a7dfc7b3e9d7e3b-1112x584.png) This difference reflects role responsibilities. Leaders typically focus on longer-term investment planning and platform capability, while practitioners are more closely tied to day-to-day execution and operational constraints. #### What this means While more than a third of teams report budget growth, a significantly larger share report rising warehouse and compute spend. As AI-driven workloads expand, infrastructure demands are absorbing much of that investment. This puts increased financial pressure on analytics teams and makes investment decisions more consequential. To stay in control, teams are prioritizing incremental processing, query optimization, and cost visibility. Efficiency is no longer optional; it’s essential to sustaining growth at scale. Cost optimization remains a priority as teams work to sustain scale under increasing workload intensity. Financial discipline in 2026 is about ensuring growth remains sustainable, not simply cutting back. ## Architecture interest exists; adoption remains limited Teams are actively evaluating modern architectural patterns, though broad production adoption remains limited. Interest in open table formats and multi-engine interoperability is now visible as a strategic consideration in complex data environments. Apache Iceberg, included in this year’s survey as one signal of that shift, shows early but meaningful engagement. In 2026, only 9% report using Iceberg in production, while 6% are planning adoption and 12% are in proof-of-concept. In total, 27% report some level of engagement. Meanwhile, 68% report no current plans. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/8d4d0a9add2f0442d869be005bcdb04a8002b0c0-1112x584.png) Among teams evaluating or adopting Iceberg, several motivations are driving interest. The most frequently cited driver is multi-engine compatibility (22%), followed by flexibility and performance considerations. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/d7a6f5665c3ba865278b1e37d9ecf381b0a8e723-1112x778.png) At the same time, organizations evaluating Iceberg report several concerns that may slow adoption. The most frequently cited barriers are knowledge gaps (27%) and unclear use cases (27%), suggesting that teams are navigating implementation complexity rather than resisting the concept itself. Interoperability and multi-engine coordination now carry greater strategic visibility, even as most teams remain in evaluation or early adoption phases as they assess maturity, fit, and long-term implications. Architecture is entering the strategic conversation in 2026, though it hasn’t yet reshaped day-to-day operational behavior. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/aaffd0ee549505650b53f4213598d13780e069f1-1112x778.png) #### What this means While more than a quarter of teams are exploring Iceberg, fewer than one in 10 have moved it into production. Interest in open table formats and multi-engine interoperability is present, but most organizations remain in evaluation or experimentation phases rather than full architectural transition. The data suggests that Iceberg is being considered as a strategic, forward-looking investment rather than an urgent operational shift. Engagement reflects long-term planning and future-proofing behavior, while limited production adoption indicates that teams are weighing maturity, tooling readiness, and internal capability before committing at scale. In the context of 2026 priorities, architecture decisions function as a secondary maturity signal—important, but not yet a defining driver of day-to-day operational change. ## What’s next: scaling trust in an era of acceleration The 2026 survey reflects analytics engineering operating under sustained acceleration. **AI is expanding output faster than governance maturity is evolving to support it. ** Today’s environment is defined by simultaneous momentum and constraint: - AI-assisted coding is embedded in development workflows - AI-generated insights are reaching stakeholders at increasing speed and scale - Infrastructure demands are rising - Trust expectations are intensifying - Data quality, ownership ambiguity, and governance gaps persist **As a result, analytics engineering now carries the responsibility of preventing AI-generated insights from amplifying existing trust gaps. **The defining production advantage in this environment will come from reliability, not volume; at scale, under scrutiny, and with clear governance. The next phase of AI integration is likely to move from generation to execution, with systems coordinating workflows, validating outputs as they act, and operating across tools with reduced human intervention. If quality controls, review practices, and governance mechanisms don’t mature alongside this shift, autonomy will increase complexity rather than reduce it. Therefore, **discipline becomes the prerequisite for autonomy. ** **Analytics engineering is increasingly defined by accountability—by the ability to scale trust alongside output. **As new capabilities expand what teams can build, trust often determines how far that expansion can scale. Organizations that succeed in the next phase will treat trust as infrastructure, embedding it into governance, data quality, and operating rhythms. **In 2026, the differentiator is discipline.** > > > — Pip Sidaway, nib ## Methodology dbt Labs collected 363 survey responses from data practitioners and leaders across industries and regions between December 5, 2025 and February 1, 2026. Of the respondents, 73% identified as practitioners, while 27% serve in management or executive roles overseeing data teams. Where applicable, results are compared to the [2025 State of Analytics Engineering](https://www.getdbt.com/resources/state-of-analytics-engineering-2025) survey to identify year-over-year shifts. Percentages are reported as the share of respondents selecting each option, unless otherwise noted. For multi-select questions, respondents could choose more than one answer, so totals may exceed 100%. In some charts, non-responses may be excluded from visualization for clarity, while percentages are still calculated as a share of total respondents to maintain consistency across the report. Percentages are rounded to the nearest whole number for readability. As a result, sums of rounded values may differ from the rounded total by one percentage point. Charts are generated from calculated survey values and then rounded for display. In some cases rounding individual components may produce totals that appear slightly different from rounded aggregate figures. The 2026 respondent base remains broadly consistent with 2025 across industries, compensation ranges, and role composition. Respondents skew toward experienced, mid-to-senior data professionals, reinforcing that the findings reflect operational and strategic decision-makers. ### Survey respondent profile Analytics engineering continues to expand across industries, regions, and organizational contexts. Respondents to the 2026 survey span a wide range of industries, including technology, financial services, healthcare, retail, and other sectors where compliance, governance, and data reliability are critical. Technology remains strongly represented, while participation across regulated and operationally complex industries reinforces the increasingly central role analytics engineering plays in enterprise environments. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/1f5eacf50caa61224afbf8c7a9dcbe3606e01e55-1112x690.png) Among survey respondents, participation remains concentrated in North America and Europe, reflecting both mature analytics ecosystems and the sourcing patterns of this year’s study. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/03cc95a9b58b7037481cda62dbfab1cd96cefca3-1112x560.png) Analytics engineers, data engineers, analysts, and data scientists continue to form the core professional composition of the field. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/24d4efe7b38d8bee2ad099251b72e7291c5f4a2f-1112x586.png) The field remains practitioner-led; 73% of respondents identify as practitioners, while 27% serve in management or executive roles. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/cd185389bd5166516f4c6e4779ca4fd297ef3517-1112x584.png) Compensation signals continued demand for analytics expertise. Among practitioners in North America, more than 80% report earning over $100K. Manager-level compensation remains concentrated in higher salary bands, particularly in North America, while regional variation persists across Europe. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/1c549d550676cee9364ce6b9391d45fe8f079a25-1112x544.png) ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/8f2a19b8816072386a66f7b422363b3e3085a73e-1112x584.png) Despite increased AI integration and heightened strategic expectations, the daily work of analytics engineering remains grounded in maintenance and organization. A majority of respondents report spending most of their time maintaining or organizing datasets. So while acceleration has reshaped workflows, it hasn’t yet displaced foundational data work. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/6d5f3863c489cb5907d55d13da7c7bba1aa79e1c-1112x584.png) #### What this means Analytics engineering in 2026 is practitioner-led, enterprise-embedded, and operating at scale. The community reflects growing strategic responsibility, balancing innovation with operational accountability as AI integration accelerates and expectations rise. #### And one more thing… Even in an era defined by LLMs and governance frameworks, some debates remain timeless. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/4b5ab26bcfcc9fa329a47646883e00459240ae9e-1112x524.png) The data suggests that alignment on data governance may be easier than agreement on lunch. Another question sparked strong opinions: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/7a0ce937e3af133a866fd9eb143f83809411a68a-1114x526.png) Responses reveal clear camps, and little consensus. Governance frameworks, it turns out, vary at 30,000 feet. Behind every dashboard and pipeline is a community navigating complexity together. And if 2026 proves anything, it’s this: The future of analytics engineering will not be defined by tools alone, but by the people who know when to trust them, and when to verify. ![Join our live 2026 State of Analytics Engineering virtual event — April 29 - 30, 2026](https://cdn.sanity.io/images/wl0ndo6t/main/8d5e21e4743a540292ce659240ab67312b66a716-3200x1800.png) *** --- --- title: "Solving the data readiness conundrum" description: "Mid-market best practices for excelling with AI and advanced analytics" url: "https://www.getdbt.com/resources/solving-the-data-readiness-conundrum" date: "2025-11-24" categories: ["Report"] --- # Solving the data readiness conundrum Mid-market best practices for excelling with AI and advanced analytics _This is a guest resource from Patrick Vinton, chief technology officer at Analytics8._ Most companies are racing to implement AI, yet lack the foundational investment that makes it work: quality data infrastructure. Reliable, standardized, and accessible information isn't merely an operational concern—it's a strategic requirement. Without this foundation, even advanced AI models fail to produce meaningful or dependable outcomes. Many businesses deploy AI for task automation and productivity gains, but leading organizations push further—using AI to strengthen decision-making across business functions. This advanced approach depends on proprietary data, transforming internal information into competitive advantage, and enabling faster, more informed choices throughout the enterprise. However, achieving this vision presents significant obstacles. Gartner’s findings indicate that 63% of organizations lack adequate data management practices for AI initiatives, or remain uncertain about their capabilities. The consequences are severe: [Gartner predicts that 60% of AI projects will be discontinued](https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk) by the end of next year, primarily due to insufficient data readiness. ## **Unstructured data: The hidden barrier to AI readiness** While tools like [dbt](https://www.analytics8.com/technologies/dbt-partners/) have transformed how we model and manage structured data for analytics and AI, unlocking AI's full potential requires organizations to also govern massive volumes of _unstructured data—_emails, documents, transcripts, logs, images, audio files, and more. This content often sits siloed in file-sharing platforms like Microsoft SharePoint. Chief data and information officers have established governance for human access to unstructured data, but now face the urgent task of adapting these frameworks to support AI consumption patterns. Mid-market firms face distinctive challenges in [preparing data for AI and business intelligence](https://www.analytics8.com/blog/how-to-make-your-data-ready-for-ai/). Positioned between large enterprises with substantial IT budgets and smaller digital-native companies operating on modern SaaS platforms, mid-sized organizations often lack the talent, tools, and resources needed to wrangle vast amounts of source data in inconsistent formats across incompatible legacy systems. ## What we learned from the mid-market To understand what prevents mid-market organizations from achieving data readiness, we conducted a targeted research study in September 2025. We surveyed business and technology leaders from 100+ North American companies spanning financial services, insurance, health sciences, and consumer products sectors. **** Respondents were grouped into three categories based on revenue performance since 2020: **Leaders**: Companies with 15%+ revenue growth **Followers**: Organizations with 1% to less than 15% revenue growth **Laggards**: Firms experiencing revenue contraction Our analysis identified six major challenge categories spanning data foundations, data transformation, and analytics tools that block AI and business intelligence initiatives from delivering business value. While our focus centers on mid-market firms, these findings apply equally to larger organizations facing similar data readiness obstacles. ## What the research uncovered **The readiness gap is substantial.** Only 14% of surveyed mid-market companies report achieving complete data readiness. More concerning, 15% indicate that 10% or less of their data is AI-ready. Performance differences are stark: 87% of high-performing companies report at least 75% of their data is prepared for AI, compared to just 11% of moderate performers. None of the declining organizations reached this 75% threshold. **Structured vs. unstructured data management shows a critical divide.** While 57% of respondents consider their organizations effective or extremely effective at managing structured data, only 41% claim comparable proficiency with unstructured data. This gap is particularly significant given that unstructured data readiness is essential for both AI and business intelligence success. **Strategic investment remains insufficient.** Data strategy receives just 14% of IT spending on AI and analytics initiatives. This underinvestment in foundational planning directly affects whether platforms, tools, and broader AI programs deliver meaningful outcomes. **Legacy tools and workflows aren’t adequate.** 40% of respondents rate their data ingestion and analytics tools as ineffective. Most troubling, nearly one in five organizations (19%) describe their tools as extremely ineffective. The data readiness gap often stems from legacy ETL tools and brittle workflows that can’t support today’s scale or complexity. Companies need modern technologies like dbt, which emphasize modular data transformation and version control, to help teams standardize and trust their data pipelines. **85% of companies say inconsistent data architecture, inadequate data hygiene, and siloed systems are the biggest barriers **— Left unresolved, these problems compound technical debt, leaving data inaccessible, unreliable, and unusable for AI applications. Relatedly, 73% of surveyed organizations point to talent shortages as a major obstacle, reflecting the scarcity of skilled professionals who can build and maintain modern data infrastructure. **Technical and organizational challenges stall AI deployment.** Technology limitations prevent 17% of AI initiatives from reaching production. Organizational factors prove equally problematic: 15% cite budget misalignment as a barrier, while 10% identify insufficient executive sponsorship. **** **** **** #### About Analytics8 [Analytics8](https://www.analytics8.com/) helps organizations make smart, data-driven decisions by translating their data into meaningful and actionable information. Our data consultants help with the entire data and analytics lifecycle — from strategy to implementation — so companies can make sense of their data and use it to solve complex business problems. --- --- title: "IDC report: The business value of dbt" description: "Learn how teams use dbt to boost developer velocity, cut rework, and replace legacy tools with scalable, governed workflows." url: "https://www.getdbt.com/resources/the-business-value-of-dbt-idc-report" date: "2025-11-14" categories: ["Report"] --- # IDC report: The business value of dbt Learn how teams use dbt to boost developer velocity, cut rework, and replace legacy tools with scalable, governed workflows. Data investments should pay off, not pile up. Get the IDC study to learn how organizations use dbt to improve developer velocity, reduce rework, and retire costly legacy tooling, all while creating more scalable and governed data workflows. You’ll see how companies accelerated time-to-market by 34%, cut annual infrastructure costs by $180k, and reduced data quality issues by 33% The report breaks down cost savings across multiple dimensions: - **Tooling and maintenance cost savings and efficiencies** from retiring legacy tools and optimizing infrastructure - **Enhanced productivity** and faster onboarding for data practitioners - **Improved data quality, trust, and timeliness** via centralized definitions, automated testing, and lineage tracking - **Accelerated time-to-insight** for analytics and AI workloads Download the full report to benchmark your current approach and explore what’s possible with the dbt platform. --- --- title: "An analyst’s guide to working with data engineering" description: "How governed collaboration between analysts and engineers enables fast, trusted, and scalable analytics." url: "https://www.getdbt.com/resources/analysts-guide-to-working-with-data-engineering" date: "2025-09-17" categories: ["Guide"] --- # An analyst’s guide to working with data engineering How governed collaboration between analysts and engineers enables fast, trusted, and scalable analytics. Data volume is exploding. It’s [estimated](https://soax.com/research/data-generated-per-day) that as much data is produced in one day in 2025 as roughly one-fourth of all data existing in 2010, and that number is growing exponentially. Speed is crucial. When the business asks a question at 9:00 a.m., the answer can’t wait until next week. However, without guardrails in place, empowering analysts to independently model data or self-serve at scale can lead to governance risks: drift in definitions, broken lineage, and compliance gaps. The fix isn’t more tickets; it’s a shared workflow. Few businesses have foundations in place to scale at the pace of data proliferation or the integrated tools to consistently streamline flow from event telemetry through database maintenance to queries and data visualization. But there is a solution: governed collaboration between data engineers and data analysts that keeps pace with the business. In this guide, we’ll explore real examples, lessons, and frameworks from dbt Labs data analysts Rachael Gilbert, Paige Berry, Chris Fiore, and Logan Cochran, as well as dbt Labs senior software engineer Zach Brown, presenting our guidance on building a collaborative environment that enables data teams to keep pace with the developing business needs. The result: fewer tickets, consistent metrics, and analysis that makes decisions. ## Why analysts and engineers need each other As mediators, analysts translate stakeholder concerns into data queries and identify developments in business data trends. But given the vast amount of often unstructured data to be queried and the speed at which stakeholders require insights, data analysts cannot work in a vacuum. From establishing critical event telemetry to structuring data infrastructures for self-service by analysts, data engineers and central data teams develop passages that guide the flow of information. Their roles ensure that analysts have access to accurate, complete data to answer questions throughout the business. On the other hand, data analysts give structure to the role of data engineers, setting parameters for business needs that inform how engineers build infrastructures. Paige Berry, lead data analyst, experienced this when she provided end-user insight into the data pipeline for a new event-tracking service, helping to structure the goals of the data engineering team. Senior software engineer Zach Brown explains, “The data team, the analysts and analytics engineers, are stakeholder number one. They’re the most important people to serve. The data team should, in my opinion, be the ones driving the decisions, saying, ‘This is the data we need collected so we can provide meaningful analysis after the fact.’” ### Better feedback loops = better data In our recent [2025 State of Analytics Engineering Report](https://www.getdbt.com/resources/state-of-analytics-engineering-2025), over 56% of respondents cited poor data as a significant challenge. Incomplete, inconsistent, or outdated data introduces errors throughout the analytics workflow, including dashboards, reports, AI models, and operational systems. Poor data quality undermines decisions and erodes trust in data teams—a situation that can be remedied with effective cooperation between analysts and data engineers. “While it's important for a data analyst to be technical and to understand how to do data modeling and some of the upstream pipeline work, ideally, that is something that's left for someone less interested in the business context and more fascinated by the technical problem of moving data through an organization. Having that division of labor gives the data engineers and analytics engineers the things they're excited to work on and full bandwidth to get to an optimal solution and infrastructure, where the data analysts can focus on the people aspect and understanding what the deeper questions are, and have time to explore that with the stakeholder.” - Chris Fiore, senior data analyst at dbt Labs Codifying feedback loops into the everyday workflow improves data quality and gives analysts and engineers a solid, shared foundation. With KPIs defined by data analysts and shared semantic layers and data catalogs to maintain consistency, engineers can focus on scalability, tracking, and compliance. Together, they ensure data is accurate and up to date, enabling scalable growth and effective data-driven decision-making. ### Saving time with self-service Surface-level insights no longer provide a competitive advantage, and deep dives into data support increasingly complex business needs. This increased technical focus is only likely to grow for analysts. With tools like dbt, analysts can create and maintain data pipelines, supporting collaboration with data engineers on analytics code, data tests, documentation, and data metrics. Rachael Gilbert, staff data analyst, explains, “Having a tool like dbt makes it a lot easier, where I can self-serve on tracing. I know this thing is failing, what feeds into it? [It helps with] figuring out why something might be failing and at least putting together a picture before I go ask someone else for help." Paige agrees, explaining the dbt offers her** **"the ability to self-serve, to explore on my own, to understand and to be able to look at the column-level lineage, and figure out where this piece of data that is giving me trouble is coming from." With analysts able to dig into the details of data, engineers are increasingly focused on enabling scale, rather than simply responding to tickets. Chris explains, "Zach's biggest focus right now is less around how we are explicitly enabling data analysts to model data and more on how we set up the proper infrastructure." Shared ownership and clear workflows allow this relationship to flourish, provided communication channels exist. ## Roadblocks to collaboration and how to break them down It is this degree of clear, consistent feedback loop and self-serve data exploration—or lack thereof—that is often the deciding factor in collaboration between data analysts and data engineers. “The ways I interacted as an analyst with the folks who were analytics engineers doing data engineering work would be what I call a data detective,” explains Paige, discussing her career before dbt Labs. But conveying this information isn’t always simple. She continues, “It was always pretty ad hoc: a lot of conversations in Slack, sometimes getting on a call to show someone live what I'm seeing, taking copious notes and screenshots and putting them into Notion. We use Loom [at dbt Labs], but [I might have sent] a Zoom recording to show what I'm seeing if I couldn't talk to someone live but it would have taken an hour to type out.” Communications are even more complicated when teams don’t “speak the same language,” which can be particularly difficult in organizations with varying degrees of technical aptitude. Documentation, lineage, and shared definitions turn detective work into a repeatable loop that others can follow and reproduce. ### Avoiding bottlenecks without losing governance When workloads bottleneck, skipping collaboration might feel faster, but it quickly erodes trust and governance. Paige finds that AI tools bridge that gap. “If I need data from a source in a staging model, at least where I can query it in Hex, sometimes I'll try to do that myself if our analytics engineers don't have capacity. When I do that kind of work, I definitely use AI to generate documentation. It saves an incredible amount of time. I always have to go back and double-check and clean up a lot of it, but it's wonderful to have that first pass already done for me.” AI tools also allow Rachael to access models that she might not otherwise leverage. While her everyday use of SQL means she’s very comfortable with it, she sometimes needs Python for a particular query. She explains, “I'm rusty now, because I'm not using it every day like I have at other jobs. [AI] does exactly what I need it to do in that regard. I don't have Python syntax memorized at this point, so it helps me get there a lot faster.” Self-service may relieve some of the burden from data engineers, but the importance of collaboration to maintain governance is still a crucial point. Zach explains, “There are a lot of different sources from different vendors. Where does that data come from and where does it end up? “One of the things that [data analysts] struggle with the most from a technical perspective is having the appropriate tools to be able to get the data they need into the right place, which, on paper, feels like a very simple thing. But our data team at dbt has such a wide-reaching breadth of data they interact with.” Without clear guardrails in place and easily catalogued data sources, this can become an extremely complicated—and error-prone—endeavor. **Roadblocks teams may face:** - Conflicting dashboards - Data from unvetted sources outside of governed environments - Difficult-to-trace errors - Non-compliance risks with legal and reputational impacts - Unknown ownership or update history **Governed collaboration principles to break them down:** - Clear documentation - Lineage tracking - Metadata management ### Scaling your team with clearly defined roles Rapid growth in data needs often outpaces role clarity. As companies scale their use of AI tools and cloud platforms, data accessibility is expanding, resulting in duplicated efforts and gaps in coverage. Data analyst Logan Cochran cites dbt Labs’s data team growth as an example, stating, “Historically, our team has been small, [but] we've [essentially] doubled in size in the last year. And I think we plan to keep growing the team, specifically in the realm of data engineering.” This amount of growth can lead to growing pains, including a lack of standardized business titles between organizations and inconsistent role documentation. As new hires bring assumptions about what it means to be a data analyst or data engineer based on their previous employers, they may not know which parts of the process are their responsibility in this unfamiliar environment. While some overlap can boost collaboration and ensure accuracy, it can also lead to wasted time—and, on the other hand, unclear boundaries can result in dropped tasks, ill-defined KPIs, and misaligned expectations. Trying to manage multiple workflows can also be taxing. A 2022 [Harvard Business Review study](https://hbr.org/2022/08/how-much-time-and-energy-do-we-waste-toggling-between-applications) found that context switching across tools can cost employees just under four hours a week, simply reorienting between applications. It also introduces more opportunities for errors—and that assumes these analysts are already skilled at navigating data engineering tasks. Chris expands on this, saying, “Oftentimes, data analysts are working across the stack, where they're at that downstream conversation, then they're doing the data viz, and sometimes they're having to go all the way upstream and do all the data validations and checking. And it can be really hard because you're wearing several different hats. You're putting on your people hat, you're putting on your data viz and designer hat, you're then having to put on your data engineer hat.” His suggestion: specialization and collaboration. “Analysts, in my opinion, really should be focused on thought partnership with their stakeholders. I should be dialed in on how my product managers are thinking, how the engineering team is thinking, and how that ties into company strategy—really understanding the business strategy and context.” He continues, “[The key is] making sure analysts can find the data they need quickly and effectively, using tools like dbt Catalog or Insights for some of the quicker asks, but having more bandwidth to deepen the partnership and thought leadership with their end stakeholder.” Zach agrees. In his view, data engineers can enable analysts to lean into their own specializations, making collaboration between the teams more about building repeatable structures than resolving one-off questions. He explains, “There needs to be that technical foundation to make it easy to collect and send all this data. There's one part, which is the process of empowering the data team to drive that across all these different projects. And then there's another part, which is that standard, and that story of what that data should look like holistically across all of our environments.” He takes a long-term view of evolution within the data team, allowing for both specialization and unity. "The analysts are the ones who know what they need, they just don't know how to make it happen," he explains. **Roadblocks teams may face:** - Dropped tasks - Ill-defined KPIs - Misaligned expectations - Duplicated efforts - Time lost to context-switching - Devalued data due to long turnaround times - Lack of bandwidth for business strategy and stakeholder concerns **Governed collaboration principles to break them down:** - Specialization of roles - Open communication between teams - Proactive, collaborative development of data architecture - Well-constructed infrastructures for self-service - Standardized, repeatable structures Governed collaboration: A structured partnership between data analysts and data engineers who work within a shared framework to co-own the data lifecycle and maintain governance principles. Governance should be enforced by the system (CI, tests, contracts, RBAC) so analysts can move fast without constant engineer oversight. Done well, governed collaboration: - Reduces bottlenecks - Builds trust within the team and with stakeholders - Improves iteration speed - Enables consistency while scaling ## Collaboration in practice: 3 lessons from dbt Labs analysts ### Lesson #1: Governed self-service saves time and supports scalability Fully autonomous self-service can introduce quality and security risks when conducted in a silo, but locking down the data pipeline is unsustainable and prevents analysts from doing their best work, especially at a speed where that work can be put into use within the business. This is where governed collaboration comes into play. Analysts ship changes within clear guardrails, and engineers steward the platform and data contracts. Platforms like dbt enable data analysts to perform self-service for everyday operations and troubleshooting, working with trusted data without relying on data engineers. With dbt, analysts propose model changes, tests, and docs in Git; CI runs checks; owners review; and lineage/freshness make impact visible. This reduces day-to-day dependence on data engineers and frees them up to collaborate on bigger-picture items—such as telemetry frameworks and infrastructure—which allows for scalability and increased productivity. As a governed environment, the dbt platform provides version control and freshness checks in the project, lineage tracking and metadata visibility in dbt Docs, role-based access necessary to maintain governance standards, and a semantic layer that centralizes metric definitions and logic to ensure data quality. ### Lesson #2: Building one shared, governed system breaks down silos Empowering data analysts to contribute to documentation, testing, and trust signals helps grow the company’s knowledge base and dramatically streamlines workflows. Logan finds that dbt enables him to work more efficiently and to share his efforts in a governed space. "I use it as frequently as I can, because I love being able to solidify some of the things that otherwise would be saved in a notebook somewhere that I'm rerunning over and over again, and that people don't have easy access to,” he explains. A governed “home” to build models that includes discovery, drag-and-drop functionality, validation, and shipping reduces thrash and tightens loops. dbt enables this with: - **dbt Catalog** provides discoverability, i.e., definitions, ownership, and trust signals, so analysts know what exists and how to use it. - **CI + version control** test every change safely (temporary schemas, dependency checks) before anything touches production. - **dbt Canvas** provides a shared, permissioned workspace to compose analyses (text, SQL, charts) alongside models, supporting discovery, drag-and-drop building blocks, validation, and a clear path to ship outcomes the team can reuse. Chris, too, has found new ways of contributing to his team’s success. "I'm starting to use more dbt Copilot to automate and speed up how I'm doing documentation and setting up some data model metadata." He continues, “Typically, as you're answering business questions or completing analytics projects, you're either needing to spin up new data models and take it from staging all the way to marks, you’re trying to answer a business question that you realize might be more routine, or you're leveraging existing models that might not have some of the dimensions that you want to add in, like plan tier or customer segment or company name.” By capturing that work in dbt Canvas or the dbt project rather than creating a one-off in a notebook, the rest of the team can contribute and reuse the work for their own projects. “dbt Catalog absolutely makes it faster to understand what data is available, what pre-existing work has already been done, what kind of logic is captured within a given model,” he says. Catalog’s health signals support this cooperative environment, flagging data freshness and quality so analysts know they’re working with information that is current and accurate, while dbt Canvas keeps the context, queries, and decisions visible to everyone under the same governance and RBAC as the rest of dbt. ### Lesson #3: One shared, governed tool speeds up ad-hoc analysis In addition to facilitating sharing, dbt tools can help analysts better understand their data, even before working with it. Logan explains, "dbt Insights makes it really easy to quickly dig in and see what the data looks like before we move into doing the actual data modeling in dbt." Through the dbt Insights interface, analysts can explore, query, and visualize data within a single governed workspace—bridging the gap between technical and business users—while allowing technical users to validate shape, quality, and semantics before any deeper modeling. All actions are version-controlled and in line with the organization’s data standards, so data teams balance self-service analytics with strong governance, maintaining compliance and consistency without loss of speed. Because speed is such a key factor, dbt Insights minimizes context switching, since users can query models directly from dbt Catalog and access metadata and documentation without switching tabs or tools. It also supports AI-assisted queries through dbt Copilot, plus natural language support, and pulls in dbt Semantic Layer metrics, reducing time wasted on fine-tuning. This deep integration and the ease with which users can preview data in a single governed environment has been game-changer for dbt Labs data analysts. Rachael enthuses, “One reason I am so excited for dbt Insights—and I will rave about dbt Insights all day—is because we've always talked about how dbt Catalog (formerly Explorer) was the place to go to understand your data. Catalog is great for documentation to get additional context or notes about a field or a model. But the other half is validating if the data you're working with is accurate, what you think it is, or what you'd expect to see querying it: what values are populating in these models and fields. So now we have dbt Catalog for the documentation of models and fields and dbt Insights for actually looking in those models and fields. That is the whole of what I need to validate and feel confident in my data.” Paige appreciates how dbt Insights empowers her to conduct her own troubleshooting, explaining, “If someone is saying, ‘I'm not seeing the data I expect to see,’ I can quickly check. Is the job failing? Maybe we don't have fresh data. Is that why, or are the sources stale in the whole transformation? Are pipelines running? Maybe there's something wrong with our connection getting data from the third-party source. That's something I can check on as part of the validation process.” Crucially, this “one shared system for self-serve analysis” doesn’t stop at the UI. The dbt MCP server exposes governed project context and safe tools—like compile/run, tests, metadata, discovery, and Semantic Layer access—over the Model Context Protocol. That means IDEs, notebooks, and agentic assistants can interact with the same trusted definitions and permissions you see in dbt Insights and dbt Catalog. The guardrails of governed collaboration enable analyst autonomy rather than restricting it, battling tool sprawl and providing the framework and common language (models, tests, metrics) and shared protocol (dbt MCP) to turn ad-hoc answers into reusable, auditable assets that move the business faster. ## 5 ways to make governed collaboration work for you Having established the benefits of governed collaboration—improved speed, scalability, autonomy, and trust—here are five ways your company can put it to use. ### Create shared development environments Separate platforms, databases, and one-off development environments are roadblocks to collaboration. Beyond the time wasted on context switching, trying to maintain lineage, consistency, and collaboration across multiple environments for a rapidly growing data team can mean confusion, frustration, and miscommunication. Paige explains, “Before dbt Insights, I would have to drop raw SQL in Slack, or a link to Snowflake where I've done something. And now I can just drop the bookmark to the Insights query, and it has everything in there the way I want it. And data engineers can immediately see the results of what I was trying to show with the query and then go back to validating.” By creating a shared development environment, data analysts and data engineers can collaborate on the same repo and propagate updates throughout the environment. ### Use Continuous Integration for testing and version control In shared development environments, speed can introduce risk. While data engineers may have a more far-reaching impact on infrastructure, even a misplaced change in analyst code or an updated model can lead to errors, broken logic, or failed validations that can ripple across the organization, leading to damaged workflows, dashboards, and, if not caught in time, even stakeholder reports. Continuous Integration (CI) automatically tests and validates code each time it is updated in the shared environment, confirming accuracy and cross-checking dependencies before the impact of a faulty change can spread. dbt Cloud’s built-in CI/CD capabilities run automatically on every PR (GitHub/GitLab/Azure DevOps), building only the impacted models in a temporary, PR-scoped schema and executing tests and dependency checks before anything merges. Engineers review diffs and run results in the PR, and Jobs handle scheduled or triggered deployment, keeping production safe. ### Make lineage and documentation upkeep a daily task Collaboration can’t work unless all parties are on the same page—but this doesn’t need to mean endless meetings or daily scrums. Keeping lineage,documentation, owners, and tests consistent in your everyday workflow helps other data team members understand updates, changes, and discoveries, preventing time-wasting duplicated work and errors from misunderstandings or a lack of information on where to find support. With many inputs and updates to track, Logan relies on this information to structure his analysis, explaining, “I look at documentation that exists and really get a solid grasp. We have such a wide-spanning project that covers so many different things that weekly, I am in dbt Catalog asking, ‘What does this column mean? What is the logic behind it? Where does it live? How does it affect everything else?’” By codifying lineage and documentation upkeep into a daily task, data teams create a time-saving paper trail, sharing information and providing a point of reference for easier collaboration. ### Establish clear handoff points Part of the collaboration between data analysts and data engineers is actually _collaborating_, which means knowing when to step back and let someone else take over. But rapidly shifting roles can complicate task ownership or even alignment on KPIs. Rachael recalls the difficulty resulting from misaligned expectations. “Where I ran into the most friction at past jobs was at larger companies when data engineering sat in a completely different org than me, and we were aligned under different leadership structures with different goals and different missions. They were very back-end data engineers who didn't really know much about analytics, and I was very much more on the data science—applying data to business decision—side of things. At the time, I was very ignorant of data engineering things, and the org structure and knowledge and skill sets just made that gap very hard to bridge.” Clarifying roles and responsibilities, as well as aligning on KPIs, provides easily understood handoff points. Teams may consider leveraging a RACI (Responsible, Accountable, Consulted, Informed) model to differentiate roles at each stage in the process, preventing redundancies and ensuring that no critical pieces are dropped along the way. ### Set guardrails to enable self-service Governance does not mean restricted collaboration. In fact, establishing clear governance principles and maintaining daily lineage and documentation updates should give data analysts more autonomy, not less. Zach explains, “Here at dbt Labs, dbt is so heavily incorporated that they have that part right. The process of self-service for analysts is super well defined.” Rather than holding analysts back, this well-defined role offers freedom to explore the data, with safeguards in place to ensure governance is maintained, checks to prevent propagation of errors, and clearly defined handoff points so that all aspects of a project are covered without unnecessary overlap. Properly deployed, governance with PR reviews, CI, contracts, tests, lineage, and RBAC in place, should allow data teams to feel empowered, not restricted. The result is autonomy for exploration, safeguards to stop bad changes, and clean handoffs. ## Your analyst enablement checklist Before employing governed collaboration for your data team, you should be able to confidently answer the following questions: ### 1. Does every analyst know how to trace lineage and trust signals (without tribal knowledge)? - If each analyst is approaching data as if no one has ever touched it before, they could be missing key background or unknowingly leveraging faulty data. It should be easy for analysts to understand the lineage of the data they are using and be able to confirm that it is complete, accurate, and up to date. - What good looks like: lineage graphs tied to owners, tests, and freshness; health/status surfaced in the same workspace where queries happen (e.g., Catalog/lineage + test results). Analysts don’t need to know orchestration internals, just whether a dataset is current, tested, and who owns it. ### 2. Are branching strategies and environments structured for collaboration? - Can your teams easily work together in a shared development environment? Is version control in place to prevent the proliferation of errors or conflicts? Your organization needs a clear staging and deployment strategy before allowing changes to a shared environment, or you risk teams duplicating efforts, or worse, breaking each other’s work. - What good looks like: PR-based workflow in Git; per-developer isolated dev schemas; consistent dev/stage/prod; CI that builds impacted models in a temporary schema and runs tests/contracts before merge; deployments handled by jobs, not ad-hoc changes in shared prod. ### 3. Are metrics centrally defined and easy to access? - It is crucial that all members of the organization “speak the same language,” agreeing on mutually delineated glossaries, categorizations, and other key features. Ill-defined database structures can quickly result in misaligned metrics, inconsistent updates, and inaccurate queries. - What good looks like: governed metric definitions (names, grains, dimensions, filters) stored in version control; a semantic/metrics layer exposed to BI/AI tools so everyone computes the same result; conformed dimensions and clear SCD handling to avoid drift. ### 4. Is governance enforced without constant intervention from data engineers? - The point of enabling self-service is to give data analysts autonomy to work without unnecessary oversight or roadblocks, but failing to adhere to governance standards can bring serious legal and reputational damage and an erosion of trust. Governance should be built into all workflows and firmly ingrained in all team members before removing consistent oversight. - What good looks like: policy-as-code (contracts, tests, data classifications), RBAC at the warehouse and workspace, column/row masking where needed, CI gates on PRs, audit logs for changes and runs. Engineers set guardrails; analysts ship safely inside them. ## Final thoughts AI has reignited the value of data, but the scale is unlike anything we’ve seen throughout history, and growing. Now is the time for data teams to build a solid foundation, securing collaborative relationships between data analysts and data engineers that draw on their respective strengths and create a supportive, well-defined partnership based on trust and shared goals. The future of analytics is shared: governed, collaborative, and fast. Paige has advice for data analysts hoping to improve their collaboration. “See if there's somebody to shadow who's doing data engineering work. What are their concerns? What does their day-to-day look like? It builds empathy and can help you understand: this is how I could adjust the way I'm describing something that makes more sense to them in their world. And see if that helps make your communication better.” As a data engineer, Zach agrees. “At any company that wants to be properly data-driven, data analysts should be the ones driving the charge. They are the experts when it comes to data. If I could be of use helping them drive that story and form that narrative, then I think that means I'm doing my job.” The teams that win make collaboration repeatable: clear roles and handoffs, self-service with guardrails, and one governed system for discovery, validation, and shipping. When analysts trace lineage, validate freshness, and iterate with stakeholders, and engineers provide the telemetry, tests, CI, and access controls, speed stops trading off against trust. AI raises the stakes but also accelerates the loop—drafting docs, queries, and checks—so long as governance is built into the path of work. Make the governed path the easiest path, and autonomy becomes an asset, not a risk. Transform your analytics workflow today. See firsthand how dbt empowers analysts with AI-powered, governed workflows. [Request a demo](https://www.getdbt.com/contact) or [start your free trial now](https://www.getdbt.com/signup). --- --- title: "Structured for intelligence: Why Al needs governed, discoverable, and provisioned data" description: "Structured for Intelligence shows how to build platforms designed for AI success." url: "https://www.getdbt.com/resources/structured-for-intelligence-why-al-needs-governed-discoverable-and-provisioned-data" date: "2025-09-15" categories: ["Report"] --- # Structured for intelligence: Why Al needs governed, discoverable, and provisioned data Structured for Intelligence shows how to build platforms designed for AI success. #### **Structured for Intelligence: Your AI Data Foundation Starts Here** AI is reshaping how enterprises work, but without governed, discoverable, and high-quality data, even the best AI projects stall. _Structured for Intelligence_ breaks down the blueprint for enterprise-ready AI systems that are accurate, explainable, and safe. ##### **Why this matters** AI projects fail 80%+ of the time, usually because data is fragmented, inconsistent, or poorly governed. This O’Reilly report shows how to fix that with structured context: unified metrics, lineage, metadata, and policy-driven access that AI can rely on. ##### **What you’ll learn** - How conversational analytics, copilots, and agentic workflows depend on strong semantics and metadata - Why governance (EU AI Act, quality checks, RBAC/ABAC) must be built in from day one - How leaders like Walmart, Block, and NBIM use MCP and structured context to scale AI safely and efficiently - A 10-step roadmap for building a compliant, AI-ready data stack ##### **Who it’s for** CDAOs, CIOs, Heads of Data & Engineering, Principal/Staff Analytics Engineers, and anyone responsible for trustworthy AI systems. ##### **What you’ll walk away with** A clear, actionable guide to: - Making AI outputs consistent and evidence-backed - Eliminating metric drift and semantic confusion - Enabling safe agentic automation - Building a governed AI stack that teams actually trust **Download the report** and get the full foundation for enterprise-grade AI. --- --- title: "2025 State of Analytics Engineering Report" description: "The 2025 State of Analytics Engineering Report highlights a sector that is evolving at breakneck speed." url: "https://www.getdbt.com/resources/state-of-analytics-engineering-2025" date: "2025-04-29" categories: ["Report"] --- # 2025 State of Analytics Engineering Report The 2025 State of Analytics Engineering Report highlights a sector that is evolving at breakneck speed. ## Introduction The rise of analytics engineering was inextricably linked to the most important computing story of the past decade—Cloud. The next phase of analytics engineering will see a transformation on an even larger scale powered by the most important computing story of this decade—artificial intelligence. We're starting to see glimmers of what that world looks like. AI is a driving force behind renewed investment and restructured workflows for data teams. **The 2025 State of Analytics Engineering Report** highlights a sector that is evolving at breakneck speed, where thus far AI is the catalyst to reshape—not replace—the role of data professionals, and where trust in data remains the foundation of success. ### Here’s what we found: - **AI is augmenting—not replacing—data teams.** Despite early fears of job displacement, data team sizes are actually increasing. 70% of analytics professionals already use AI to assist in code development, and 50% use AI for documentation. AI adoption is surging, but rather than replacing human expertise, AI is changing what jobs look like. - **Investment is back—big time.** After a period of economic caution, data budgets are growing again, and AI is leading the charge. AI tooling is, by far, the biggest area of investment for data teams in the past year. - **Building trust in data is still the top priority.** Even as organizations embrace AI, data teams recognize that unreliable data means unreliable outputs. Data quality remains the most critical challenge for data teams to solve. The **2025** **State of Analytics Engineering Report**, crafted by dbt Labs, examines how data teams are navigating this new AI era. This report isn’t just a snapshot of where we are today. It’s a blueprint for where analytics engineering is headed next. Let’s dive in. ![Advertisement promoting the on-demand 2025 State of Analytics Engineering virtual event.](https://cdn.sanity.io/images/wl0ndo6t/main/eacfdb3005782bfbed34c8f2386699fcd3c6d7c7-1200x630.png) ## Key Insights ### About organizations Analytics engineering is proving its value in new and varied domains—expanding far beyond the initial "tech-forward early adopter" userbase that powered its initial base While the[ tech industry ](https://www.getdbt.com/case-studies/symend)is still the most heavily represented by a substantial margin (at 34%), its share of the total has declined by 3% year-over-year. Some of the most heavily regulated industries—[financial](https://www.getdbt.com/case-studies/bilt-rewards) [services](https://www.getdbt.com/case-studies/rocket-money) (15%) and [healthcare](https://www.getdbt.com/case-studies/fullscript) and [life sciences](https://www.getdbt.com/case-studies/lundbeck) (10%)—represent a significant portion of the analytics engineering community. Analytics engineering is proving to be a critical capability even for organizations dealing with complex, compliance-heavy data ecosystems. ![Chart from dbt Labs' 2025 State of Analytics Engineering Report with results to survey question, "What best describes your organization's industry?"](https://cdn.sanity.io/images/wl0ndo6t/main/3c1f8452f5c12682d91f48daaef39bf7abca36bd-2400x1260.png) ### Data practitioners #### Salary The view this year is one of growing investment in data teams, and this extends to financial compensation. Salary distributions show strong growth, although this has been driven by and concentrated in North America and Europe. ##### North America - In last year’s survey, 69% of individual contributors in data roles earned over $100K. This year, it’s over 80%. - Similarly, 32% of respondents in manager roles reported earning over $200K last year; this year, it’s 49% of manager respondents. ##### Europe - Last year, 40% of individual contributors reported earning over $50K. This year, it’s up to 51%. - A potentially interesting thread to track is that respondents reported a decline in manager salaries in Europe, however. Last year 79% of respondents reported earning over $100K; this year it’s 51%. The large number of ICs relative to managers means that this does not significantly alter the picture in aggregate. ![Chart from dbt Labs' 2025 State of Analytics Engineering Report with results to survey on Individual contributor salary by region (in USD)](https://cdn.sanity.io/images/wl0ndo6t/main/632f111a608cf9467c0acc42b3cb69c08a2708f1-2400x1260.png) ![Chart from dbt Labs' 2025 State of Analytics Engineering Report with survey results on manager salary by region in USD](https://cdn.sanity.io/images/wl0ndo6t/main/af9cb33d5a0f21f2e342342f48051ec6a36b9d31-2400x1260.png) ## Investment areas We asked respondents how their data teams are thinking about investment in data platforms and various data tools. AI tooling is by far the largest area of tool investment, and the only category in which the plurality of respondents (45%) plan to increase investment in the next 12 months. Data quality/observability was the second-largest area for increased investment; 38% of respondents are planning on increasing investment in the next 12 months. The interest in increased investment in data quality/observability highlights the urgency with which organizations are trying to solve problems related to data quality. This is a theme that permeates throughout the findings of this report. ![Chart from dbt Labs' 2025 State of Analytics Engineering Report with results to survey question, "How is your team thinking about investment in the next 12 months? Part 1"](https://cdn.sanity.io/images/wl0ndo6t/main/7ddf74ff68faa86e7cd1a968562c387f40383603-2400x1260.png) ![Chart from dbt Labs' 2025 State of Analytics Engineering Report with results to survey question, "How is your team thinking about investment in the next 12 months? Part 2"](https://cdn.sanity.io/images/wl0ndo6t/main/8e3798e59c8cc0ced84ccadac5e3863296a276e9-2400x1260.png) ## AI Investment **We are clearly in the AI era.** An overwhelming majority—80% of respondents—are already using AI in their day-to-day workflow. This represents an enormous jump from just one year prior, when only 30% of respondents were doing so. ![Chart from dbt Labs' 2025 State of Analytics Engineering Report with results to survey question, "In which of the following ways do you use generative AI in your workflow?"](https://cdn.sanity.io/images/wl0ndo6t/main/f1925b84800668d26813f9c38fc94f19e2f080cf-2400x1260.png) **How are respondents using AI?** Code development, followed by docs / metadata development represent the most frequently reported use cases for data teams. That is, today AI is much more commonly being used to create or describe data assets, than to consume them in order to help answer data questions. Overall, 70% of respondents use AI for analytics development in some form. How are they going about this? Data teams are primarily using general-purpose, widely accessible LLMs like ChatGPT, Claude, and Gemini to handle code development. However, these general-purpose tools can often run into limitations due to constraints on their ability to view important context in an analytics project, such as the rest of a codebase or metadata. It’s no surprise we’re already seeing ~25% of respondents use specialized gen AI solutions built into their development tooling. **We expect two things to happen here:** 1. The context from your data will make its way into general purpose systems via the development of open standards for managing context to LLMs. 2. Specialized tooling that is deeply integrated with data development use cases will become more central in the workflows of data practitioners. ![Chart from dbt Labs' 2025 State of Analytics Engineering Report with results to survey question, "Which of the following best describes how you use AI tools in your analytics engineering workflows?"](https://cdn.sanity.io/images/wl0ndo6t/main/31eaa9092e5dd754bcbd30560f79bb703babb082-2400x1260.png) Meanwhile, the appetite for using AI to query data in natural language is there. More than half of respondents reported being interested in doing so, but can’t (or won’t) yet. 29% of respondents said their organizations don’t use AI tools for natural language interaction with data, but would like to, while a further 23% have already experimented with natural language interaction. ![Advertisement promoting the 2025 State of Analytics Engineering Report available for download](https://cdn.sanity.io/images/wl0ndo6t/main/1f277773d268793d2da40b8bab1029df154ac295-1940x180.png) The difficulty of generating consistently accurate data reporting with AI is a likely factor here. Of the 30% using AI to consume data assets by answering questions in natural language, two-thirds are doing so today using vanilla SQL generation, compared to one-third using a semantic layer. [Research has in the past indicated that using a semantic layer tends to lead to significantly higher accuracy in natural language AI queries of data.](https://www.getdbt.com/blog/semantic-layer-as-the-data-interface-for-llms) As noted earlier in the study, 27% of respondents intend to increase investment in semantic layer tooling in the next 12 months. Meanwhile, nearly a third of respondents said they are already using AI tools to interact with their data using pre-bundled tools, third-party platforms, and homegrown tools. The level of interest indicated by organizations in going down this route means this number will likely continue to climb. ![Chart from dbt Labs' 2025 State of Analytics Engineering Report with results to survey question, "Which best describes your organization's use of AI tools for natural language interaction with data?"](https://cdn.sanity.io/images/wl0ndo6t/main/5fcbb47853aeafbe6304ca8a3d135e4ea395a160-2400x1260.png) When it comes to optimism around the impact of AI, while a small percentage of respondents are skeptical, the majority express a positive outlook. The vast majority of analytics professionals see potential value in AI tools, but are still somewhat cautious in their optimism. ![How optimistic are you about the potential value of AI in the data analytics workflow?](https://cdn.sanity.io/images/wl0ndo6t/main/d410fdc7d5e1ca0208423fb5bcba552cf12982be-2400x1260.png) **AI/LLMs hold promise for data teams, but practical impact is still evolving.** The small but notable gap between optimism and real-world impact suggests that while AI-driven tooling is improving workflows, it hasn’t yet been quite as transformative as hoped for all of its users. Based on the pace of adoption we’re already seeing, we will watch closely how this impact changes in next year’s report. ![If you use AI tools as part of your data workflow, how impactful are these tools?](https://cdn.sanity.io/images/wl0ndo6t/main/9254f1cf301e3b72195b920031060dd02ea223a9-2400x1260.png) Survey respondents expect a wide range of areas where AI will significantly benefit the data workflow in the near future. In particular, a great deal of optimism (cited by 56% of respondents) is expressed for the ability of AI to help with self-serve data exploration for more users, and to help bridge the data quality gap with proactive data monitoring (cited by 50% of respondents) and pipeline debugging. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/da8505d12a0bedbe720f5aacb2ff5bb216c1f2fc-2400x1260.png) We see substantially more optimism for each individual use case than we did last year, suggesting growing conviction in the impact of AI. For instance, last year 33% of respondents expected AI would make it easier to debug pipeline issues; this year it’s 50%. Similarly, the number that said they expected AI would significantly benefit the process of analytics code increased to 57% this year from 50% last year, and 54% of respondents expect AI to benefit testing/tracking code, up from 47% last year. ## Data team budgets and headcount With the increases in AI investment, data budgets and data teams are growing significantly, according to survey respondents. Last year, just 9% of respondents reported budget increases for their data teams; this year 30% of respondents reported budget increases from the previous year. ![Is your data team's budget bigger or smaller than it was last year?](https://cdn.sanity.io/images/wl0ndo6t/main/63ebced4594c422e13c3176ae9bbce7c72c7ebb1-2400x1260.png) AI and data tooling investments aren’t coming at the expense of headcount; data teams are growing too. 40% of respondents reported headcount increases in the year, compared to 14% of respondents in last year’s report. Data teams are an important part of an organization’s success, and investments are demonstrating that in this year’s survey. ![Is your data team bigger or smaller than it was last year?](https://cdn.sanity.io/images/wl0ndo6t/main/63384d71e5a6db7fe98f21bad2007a328cd4cf60-2400x1260.png) ## Challenges for data teams Data teams solve a variety of challenges for their teams and organizations. The survey results underscore a reality for data teams: increasing trust in data is the most important objective for data teams. This has not changed in the AI era. The ability to ensure accuracy, transparency, and governance in the data workflow is more than a technical necessity—it’s a business imperative. ![Rate the importance of objectives for your organization](https://cdn.sanity.io/images/wl0ndo6t/main/69fa4669d8bf112787675370aa629496231d481f-2400x1260.png) Poor data quality continues to be the challenge most frequently reported by data teams, cited by over 56% of respondents. When data is incomplete, inconsistent, or outdated, it introduces errors that ripple across dashboards, reports, AI models, and operational systems. Poor data quality compromises trust and decision quality, and severely hinders the effectiveness of data teams. While documenting (25%) and maintaining data sets (12%), as well as constraints on compute resources (9%)—all once major hurdles—are now significantly alleviated challenges, the problem of poor data quality stubbornly persists. Meanwhile, the prevalence of ambiguous data and poor stakeholder data literacy as significant pain points suggests that a major challenge isn’t just technical but also organizational. Data quality is irrelevant when organizations are unable to make use of that data to drive meaningful insights. ![What do you find most challening while preparing data for analysis?](https://cdn.sanity.io/images/wl0ndo6t/main/8d1ae7e2cc3351e770926320e9227ec157cc7428-2400x1260.png) ## Data practitioners’ daily experience Even as more and more data teams adopt AI in their workflow, this has not fundamentally changed how contributors report spending most of their time. 57% of respondents spend most of their workdays maintaining or organizing data sets, nearly identical to last year’s report. ![Which of the following best describes how you spend most of your time?](https://cdn.sanity.io/images/wl0ndo6t/main/8a6a395e92468028d255d19d35183c10619ed277-2400x1260.png) Similar to last year’s report, enabling other teams was identified as the primary measure of success for data teams. ![How does your team primarily measure success?](https://cdn.sanity.io/images/wl0ndo6t/main/309086782759378b3500792d5d4c0716de9a1b38-2400x1260.png) The creation of analytics engineers was substantially driven by data analysts looking to draw best practices from software engineering. Today, we continue to see the pull for nontechnical business users towards engaging in data transformation. Nearly 65% of respondents said that enabling nontechnical business users—most of a data team’s stakeholders—to create transformed and governed data sets would somewhat or greatly improve their organization’s data value and efficiency. ![How helpful would it be for your organization to enable nontechnical business users to create transformed and governed datasets?](https://cdn.sanity.io/images/wl0ndo6t/main/853272fa5932311ef7c9f67ec28a637254f5e4e5-2400x1260.png) Work is distributed among data teams in a variety of ways—by function (data engineering, data science, etc.), by business area (marketing, sales, finance, etc.), by project, or by a hybrid approach. Last year we noted an increase in the hybrid model in which work is distributed by both business area and function. This signaled that data team members of all specialties are embedding more deeply within their organizations. This has held true in this year’s survey as well. ![What determines how work is distributed within your data team?](https://cdn.sanity.io/images/wl0ndo6t/main/77f393f3a56d9fd5d267622187f1c964fd34458a-2400x1260.png) Respondents overwhelmingly agree their organization values the data team. Respondents feel less strongly that their organizations set clear goals for the data teams. Data teams feel valued, but ambiguity remains in how data teams are expected to impact their organizations. ![My organization values the data team.](https://cdn.sanity.io/images/wl0ndo6t/main/5064be55e259250d0a0483fc98861122d09d912c-2400x1260.png) ![My organization sets clear goals for the data team.](https://cdn.sanity.io/images/wl0ndo6t/main/baab7620550936ac2ed33a80c8ae9174df6c398e-2400x1260.png) ## What's next? The 2025 State of Analytics Engineering makes one thing clear: Data teams are not standing still. With AI reshaping workflows, investment flowing back into data, and maintaining data quality still a key challenge, organizations must be proactive in defining their data strategy. This report is not just a reflection of current trends—it’s a call to action. Data leaders should use these insights to guide their investments and to focus on AI-driven efficiencies while reinforcing governance to build data trust. The path forward is one where AI and human expertise work in tandem, data reliability is non-negotiable, and the role of analytics engineering continues to expand in impact and influence. Now is the time to take these learnings and build a data strategy that drives smarter decisions, faster innovation, and a more resilient data organization. ## There's more There’s so much to explore within the analytics workflow. Explore [Tristan Handy’s whitepaper on The Analytics Development Lifecycle](https://www.getdbt.com/resources/guides/the-analytics-development-lifecycle) on applying software engineering best practices to all layers of the analytics stack. And check out Tristan’s post [How AI will disrupt data engineering as we know it](https://www.getdbt.com/blog/how-ai-will-disrupt-data-engineering). **** ## And one more thing... Frankly, your families and friends are doing pretty well, based on our personal experience. ![Do your family and friends know what you do for a living?](https://cdn.sanity.io/images/wl0ndo6t/main/4debd791bf76390125d4770e4ef4ccea3daaf8c7-2400x1260.png) A strong showing for “mood.” ![How do you pronounce "data"?](https://cdn.sanity.io/images/wl0ndo6t/main/30033d2bf77eb9d340514781c7eacee9d7f39ca1-2400x1260.png) There’s a right answer here. ![When you're looking for your Lyft/Uber car, what do you look for?](https://cdn.sanity.io/images/wl0ndo6t/main/10acd9038381ee93f8f14bdbbb4bd3c7a079b60b-2400x1260.png) There’s a right answer here, too. ![Cake or pie?](https://cdn.sanity.io/images/wl0ndo6t/main/e6927a4370d4a51b87ae1f0b593eb08ec19227e7-2400x1260.png) ## Methodology dbt Labs collected survey responses October 8, 2024 - December 27, 2024 from 459 data practitioners and leaders—70% of survey respondents are individual contributors (IC) and 30% are managers. Analytics engineers made up 48% of IC respondents, 36% of IC respondents are data engineers, and 16% of IC respondents are data analysts. These splits are virtually identical to the 2024 State of Analytics Engineering. ## Download the report The 2025 State of Analytics Engineering Report, crafted by dbt Labs, provides a comprehensive overview of how data teams are navigating the AI era. Complete the form below to download the full report. ![Advertisement promoting dbt Labs' 2025 State of Analytics Engineering virtual event on April 30.](https://cdn.sanity.io/images/wl0ndo6t/main/eacfdb3005782bfbed34c8f2386699fcd3c6d7c7-1200x630.png) --- --- title: "The Analytics Development Lifecycle (ADLC)" description: "Explore how software engineering practices can enhance all layers of analytics." url: "https://www.getdbt.com/resources/the-analytics-development-lifecycle" date: "2025-04-29" categories: ["Guide"] --- # The Analytics Development Lifecycle (ADLC) Explore how software engineering practices can enhance all layers of analytics. ## A letter from our Founder & CEO In 2016, I wrote a blog post entitled “[Building a Mature Analytics Workflow](https://www.getdbt.com/blog/building-a-mature-analytics-workflow).” In it, I compared analytics to software engineering and stated my position that we should be bringing software engineering best practices into our work as data practitioners (version control, CI/CD, testing, documentation, etc.). Before that, this point of view was counterintuitive and not widely accepted. Fast forward to today, and that post helped launch a [community](https://www.getdbt.com/community) and a [product](https://www.getdbt.com/product/dbt-cloud), and many of the assertions it made have been accepted as best practice in the data industry. However, nearly a decade later, it is clear to me that the original post is in need of an update. Why? First, we now have the collective experience of tens of thousands of companies applying these ideas. We can observe from dbt product instrumentation data that a large majority of companies that transition to the cloud adopt at least _some_ elements of a mature analytics workflow—particularly related to data transformations. But what about the other layers of the analytics stack? Here is what I mean: - At your company, do you believe that notebooks and dashboards are well-tested and have provable SLAs? - Do your ingestion pipelines have clear versioning? Do they have processes to roll back schema changes? Do they support multiple environments? - Can data consumers request support and declare incidents directly from within the analytical systems they interact with? Do you have on-call rotations? Do you have a well-defined incident management process? The answer to these questions, for almost every company out there, is “no.” The fact is that we—the entire data community—have not rolled out these ideas to all layers of the analytics stack, and this leads to bad outcomes: impaired trust in data, slow decision-making velocity, low quality decisions. **We have pushed back this tide within the narrow domain of data transformation; it is time to apply these lessons more broadly across the entire analytics workflow.** We need to collectively acknowledge that _we are not done_, that there is further to go on this journey. The goal of this paper is to outline the workflow principles that I believe are the solution to this problem. These principles apply to all analytical jobs-to-be-done; data maturity is an end-to-end effort. We have achieved so much together over the past decade. I look forward to another decade of progress. — Tristan Handy, September 2024 ## Intro: A mature analytics workflow Analytics is the practice of analyzing data to make truth claims. These truth claims can be: - Descriptive (“We had 200 orders yesterday”) - Causal (“Revenue is down because our ad inventory is low during the summer”) - Predictive (“We estimate that revenue next quarter will come in ahead of plan”) - Prescriptive (“Use the following ad copy to this segment to maximize click through rate”) If you are using data to make truth claims, you are practicing analytics. To practice analytics **well**, you need two things: 1. An analytical system (tools and technology) 2. An analytical workflow (process) These two things work together to create your analytics _practice_. ![Graphic showing two main components of an Analytics Practice: an Analytical System (tools and technology) and an Analytical Workflow (process), each represented with icons.](https://cdn.sanity.io/images/wl0ndo6t/main/c65bcb3ccd98fbafd92237b2fe83a736c1be2ae4-1448x740.png) Some analytical practices are better than others. And some are simply different: they make different tradeoffs. For example: velocity and governance are commonly traded off against one another. Spreadsheet-based ad-hoc analysis can be a fast way to get to an answer, but its governance characteristics are typically low. We believe that teams are capable of practicing analytics in a way that hits a far higher mark on these dimensions simultaneously. This requires both better analytical systems and more mature analytical workflows. Because the reality is that despite technological and process advancements in the past decade, it is still quite uncommon to see mature analytics principles applied to all areas of analytics—from data ingestion to orchestration, observability, discovery, and analysis. As a result, analytics in practice still suffers from many of the same problems it did a decade ago: low velocity, inaccurate results, impaired trust…all without proper cost control. Progress has been made, but there is more we can do. This paper focuses on the workflows part of the equation. In it, we propose a specific workflow designed to accelerate data velocity while improving data maturity:** the Analytics Development Lifecycle (ADLC).** We believe that implementing the ADLC is the best path to building a mature analytics practice within an organization of any size. ## Requirements of a mature analytics workflow A mature analytics workflow has the following characteristics. ### Data scale How much data can be processed? A mature workflow requires analytical systems that can scale up and down elastically, abstracting away the complexity involved in processing data sets of any size. ### Collaboration scale How many users can effectively collaborate together? A mature workflow is suitable for a single user and scales to arbitrarily many. As additional users are added to the process, design considerations may change, but the fundamental workflow does not. ### Accessibility How many _types of _users are capable of using this system? A mature workflow brings different personas together to collaborate as peers. ### Velocity How quickly can a user conduct a given unit of analysis? A mature workflow does require participants to undertake _some overhead_ relative to simple ad-hoc work, but both minimizes this overhead and_ injects velocity_ as requirements scale beyond the basic. ### Correctness What is the likelihood that a given output produced is correct? A mature workflow not only produces correct results, it also contains mechanisms to automatically validate correctness. ### Auditability What changes have occurred to produce a given result? A mature workflow produces artifacts with changes tracked and outputs reproducible at any point in time.  ### Governance Can we assert that the right people are using data in accordance with all applicable rules and regulations? A mature workflow integrates governance directly and from the outset.  ### Criticality Can the business rely on the results of this workflow? A mature workflow produces artifacts that can seamlessly scale from experimental to mission-critical without needing to be re-built. ### Reliability What is the likelihood that the system will operate without failure for a specified time period? A mature workflow requires systems that are resilient to failure and provide uptime SLAs that allow the business to depend on them. ### Resilience Do errors result in massive or minimal business impact? Errors are inevitable in all complex systems, and a mature workflow must anticipate them, minimize their impact, and have mechanisms to quickly remediate them. ## Stakeholders of the ADLC There are three primary personas of individuals that participate in the ADLC: ### The engineer The engineer creates reusable data assets: pipelines, models, metrics, etc. The engineer is primarily focused on creating data assets_ that others will use_ to create business value. ### The analyst The analyst performs analysis that drives decision-making. The analyst does not make decisions; their role is quantitative investigation, and they present analysis and/or recommendations to the decision maker. ### The decision-maker The decision-maker is responsible for taking quantitative outputs and translating them into action for the business. This does not imply seniority: decision-makers include everyone from a campaign manager optimizing segmentation to a CEO directing the resources of an entire company. Taken together, these three personas cover every individual at an organization who interacts with data for the purpose of analytics. These personas are not job titles. The ADLC does not require any particular mapping between personas and job titles; different organizations can decide on appropriate job descriptions based on their own unique organizational contexts. For example, a single individual could act as each of these personas at a small startup, while at large companies there may be many job titles that fall along the above continuum. What is important to the ADLC is how these personas work together. Specifically, the ADLC requires two things. 1. These personas must all collaborate together using a common workflow: the ADLC. The ADLC is not just for engineers and not just for analysts. It is a multi-persona workflow wherein every knowledge worker in an organization collaborates together. 2. These personas must all collaborate together within tooling that empowers each of them to perform their assigned roles according to the ADLC. This might seem obvious given the above point, but in practice this is a major gap in the tooling ecosystem today. While tooling for the engineer has made significant progress over the past decade along its workflow maturity, tooling for the analyst and the decision-maker has largely not kept pace. We see this as one of the biggest barriers to full adoption of the ADLC today. ### Hats, not badges One of the failure modes of an analytical practice is excessive segregation by persona. Because the ADLC is fundamentally an integrated, iterative process, segregating tasks too strongly causes friction and therefore slowness. For instance, if an analyst has to go to an engineer to source new data, that engineer will likely put a ticket in their queue, then prioritize it, then eventually get to it. This can inject weeks into a process that could otherwise take minutes. While the ADLC recognizes these three personas—the engineer, the analyst, and the decision-maker—it also encourages us not to see these personas as static. For example: - Analysts get pulled into engineering work to unblock themselves and move faster. - Decision-makers get pulled into analytical work to drill into any analysis provided and ask follow-up questions. - Many jobs are analyst / decision-maker hybrids, where the same person is both analyzing and decisioning on data. The above three personas are like hats data practitioners put on and take off, not badges we wear all day every day. We have our primary hat, the one we like wearing best. But over the course of the day, as we get pulled into solving real problems, we need the flexibility to put on different hats. ![Hats not badges](https://cdn.sanity.io/images/wl0ndo6t/main/54f174e86df7e9a255de1df27f24a056f463acd4-1186x1186.png) The most effective data practitioners can wear all three hats. And the best data tooling enables as many people as possible to wear all three hats. Even with great tooling, you will still have a hat you prefer. But the ability to wear all of them as the situation demands allows you to complete a single end-to-end task yourself, without getting stuck behind someone else’s queue. The best organizations _encourage_ talented people to flex between these different personas. They allow them to take an idea and get curious about it, to explore it without needing to file a ticket or wait for anyone else. This, in turn, requires tooling that prioritizes both _accessibility _and _workflow maturity _at the same time. Rather than saying “stay in your lane,” we should be saying “here are tools that allow you to get your job done _in a mature way_.” ## The ADLC model We propose a simple, straightforward model for the Analytics Development Lifecycle (ADLC). This model governs changes to, maintenance of, and use of any analytical system. The ADLC is heavily informed by a single guiding principle: **analytical systems are software systems**. Therefore, in developing large-scale, mission-critical data systems, many of the best lessons that can be learned come directly from software engineering. As such, the ADLC borrows very intentionally from the [software development lifecycle (SDLC)](https://www.browserstack.com/guide/devops-lifecycle). There is no one single canonical version of the SDLC, but here is a common visual depiction. ![DevOps Lifecycle](https://cdn.sanity.io/images/wl0ndo6t/main/6e46d72cc8e129217deac3078767c9e6961c72f6-792x440.jpg) Since its inception, the SDLC has been broadly adopted globally and across industries—it is widely understood and battle-tested. As such, it is a good framework from which to draw upon. In the following sections, we will propose a model for the ADLC that borrows heavily from the SDLC. The ADLC is not relevant only to a single part of an analytical system: it is relevant to the entire system, from ingesting data to transforming it to analyzing it to building applications on top of it. This paper takes care to describe the work being done as creating analytical ‘assets’ or ‘artifacts’—these can be pipelines, models, dashboards, notebooks, or any other object that creates, moves, processes, or analyzes data. In this model, there are eight discrete stages: 1. Plan 2. Develop 3. Test 4. Deploy 5. Operate 6. Observe 7. Discover 8. Analyze As in the SDLC, the relationship between these stages is a loop. Here is how the stages logically relate to one another: ![Infinity loop diagram illustrating the Analytics Development Lifecycle (ADLC), showing key stages from develop and test to deploy, plan, analyze, operate, observe, and discover.](https://cdn.sanity.io/images/wl0ndo6t/main/6a2eca8c4be8bc95297a2c67c14b3a4aa8e862a4-1850x906.png) Next, we will examine each of these phases. Before diving in, it is important to set realistic expectations. There have been many books written about each one of these stages in the SDLC. Entire books just about writing code, just about operating production systems, etc. This paper will not even come close to a partial treatment on any of these topics; our goal here is to _outline the framework_ as a jumping off point for further work, writing, and community contribution. ## Plan Analytical systems collect data from and build models of the real world, and the real world changes constantly. As a result, changes to analytical systems are _constant_. The Plan phase is the beginning of the process of making changes to an analytical system. There is no one-size-fits-all approach to the Plan phase. Changes to an analytical system come from many different places. New business requirements. Issues identified in production. Refactoring. Some changes may be tiny and some may be huge. The larger the change, the more critical it is to run through a thorough planning process. Best practices include: ### Create and validate the business case All changes to an analytical system should be based on a solid business case, and often clarifying, documenting, and aligning on that business case is both the most important step in the process and yet the most often skipped. Have a clear process by which changes above a certain threshold must go through before getting worked on. ### Create your implementation plan Determine what shared functionality can be referenced or extended, whether from the community or from others in your org. Extending existing assets can require more up-front effort (because of the testing and coordination required) but over the long term, maintaining multiple similar copies of assets creates a huge drag on a system. Keep your code [DRY](https://www.getdbt.com/blog/dry-principles). ### Get stakeholder feedback Check back in with the stakeholders identified when building the business case to get their comments and buy-in for your proposed approach. Missing this feedback cycle can lead to a lack of alignment and wasted time and effort. ### Create a test plan Before writing a line of code, answer how you will assert that the code you wrote is functioning as intended. What use cases does it need to handle? What problems does it need to be robust to? ### Anticipate downstream impacts If changing an existing asset, identify the downstream consumers of that asset (other assets and other humans). Develop a plan for how to make changes in a non-disruptive way, including testing downstream assets. Create a deprecation plan for older functionality being replaced that will introduce breaking changes. ### Plan for maintenance Historically, most analytical assets were temporary, throwaways. A single spreadsheet attached to an email, ready to be replaced by a new copy next week, disconnected from any larger system. This is not how mature analytical systems are built today. Today’s analytical systems are interconnected and long-lived, just like software systems. And as in software systems, most of the work involved is in the maintenance phase, not in the initial development phase. So, prior to starting the work, make sure to plan for maintenance. Who should be maintaining the changes you are making over the long term? Are you changing an asset that you built originally? That someone else built? That is owned by your team or another team? Etc. ### Determine access levels What teams and individuals should have access to the work? Is there personal identifiable information (PII) or sensitive personal information (SPI) in the data that you are working with and how does it need to be handled? ### Implement larger changes in small pieces Divide up larger work into smaller units that can be taken through the development process and merged independently. The ADLC is iterative: faster, smaller iteration cycles tend to produce healthier analytical practices. ## Develop The Develop phase is what gets most of the attention in the ADLC, but it is actually overrepresented relative to the amount of time spent in this phase. In practice, with a high-quality Plan phase, the Develop phase should move fairly quickly for an experienced practitioner. It is focused on translating the knowledge you gathered in the Plan phase into code. Best practices include: ### Code first Whatever type of analytical asset is being built, and whatever persona is building it, the tool you use should read and write human-readable code. _Code doesn’t have to be the user interface_, but it does have to be the underlying representation of all business logic in order to live up to the ADLC. This is for the following reasons: - Code can be edited by multiple tools, used by multiple personas. - Code can be checked into source control systems that enable mature collaboration across thousands of co-contributors. - Code can go through the CI/CD process. - Code, as language, is composable and therefore maximally expressive. ### Choose and customize your own development workflow There is no ‘correct’ development workflow or toolset. You can use emacs or vim, CLI or IDE, a graphical user interface or an AI copilot, and you can switch between these different modalities as the situation demands. _You are the best judge of how you are maximally effective._ What is critical is that your analytical system supports contributing code in multiple modalities as suitable for multiple personas. Highly productive developers not only choose the tools that best suit them, they customize them, sometimes extensively. This includes everything from hotkeys to color schemes to macros and plugins. The difference between developer productivity within a ‘vanilla’ development environment and a development environment that has been tuned to your specific workflow can be dramatic. ### Adhere to a style guide Many choices made when writing code are _stylistic_: capitalization, indentation, etc. While there often is no ‘correct’ answer on these topics, it is important that they are done consistently across a code base. Create a style guide to define this consistency and then invest the time to follow it—doing so will improve the productivity of every single developer that interacts with your code base. ### Prioritize functionality over performance Your initial job is to write code that meets your functional requirements. Once you do that, you can prioritize performance as much as is appropriate. ### Invest in code quality Most of the time spent in any given analytical system is on its maintenance, not on its original development. Set up yourself and others for success: _write good code_. This does not just mean following the style guide. It means: - Writing code that is idiomatic to the language you are using - Applying common design patterns - Using descriptive names - Prioritizing readability - Writing in-line comments and supporting documentation - Keeping code DRY - Designing for reusability - …and many other best practices ### Get code reviewed No code should get merged into production without a second set of eyes on it. Invest in code review in your organization and make sure the process is rigorous and consistent. Make sure peers are incentivized and organized in a way that enables them to review others’ work. ### Use standards to avoid lock-in Writing code in proprietary languages risks vendor lock-in. Using open languages (such as SQL and Python) and frameworks (such as Apache Spark and dbt) that are open is highly preferable. Code bases live for a long time—often far longer than the lifetime of any one single product or vendor. ## Test The Test phase is an absolutely essential part of the ADLC. This is one of the areas in which immature analytics workflows often fall furthest from the mark. We believe that no production analytical artifact should exist without tests. In practice, the data pipeline space has made significant progress in testing over the past decade, but it is still quite uncommon to see well-tested notebooks and dashboards. This represents a significant opportunity for increased maturity in the current ecosystem. The Test phase falls immediately after the Develop phase and refers to testing changes _before they are merged into production_. Data is also tested on an ongoing basis _in the production environment,_ but the ADLC considers this the Observe phase. Of course, implementing good tests when writing code typically forms the backbone of effective observability. The Test phase spans two distinct workflows: 1. Iteratively, interspersed with development. 2. Automatically, as a part of the pull request process. This is known as continuous integration (CI). Changes should never get merged into production without CI. There are three types of tests that should be implemented: ### Unit tests Test _only _the logic being implemented, not the underlying data, and not the system as a whole (“Will this model do what I expect it to do?”). ### Data tests Test the logic being implemented plus the underlying data (“Does the data conform to my expectations?”). ### Integration tests Test the system as a whole (“Do my changes break any other parts of the system?”). As in software engineering, writing tests is not _hard_ (with appropriate tooling), but it can be painstaking. But a well-tested codebase significantly reduces the maintenance burden of an analytical system as errors can be quickly traced to their source. Testing is as much about culture, shared expectations, and accountability as it is about tooling or technique. The desire to skip writing good tests and move on to the next task is always present and must be balanced via accountability mechanisms like code reviews, linting, and test coverage metrics. In software engineering, there are advocates for different testing methodologies, such as [test-driven development](https://en.wikipedia.org/wiki/Test-driven_development). The ADLC does not specify a particular testing methodology, only that no production analytical asset should exist without tests. It is common and appropriate for developers to focus on testing their own code during development and then running the entire test suite during CI. ## Deploy The Deploy phase is where code is migrated from development to production. This can be a fairly straightforward single hop, or it can be more complicated depending on the needs of the system. All deployment processes should have a set of common characteristics: ### Deployment is triggered based on a merge in source control As source control stores the state of the repository, branches represent the state of environments. Before deploying code to any environment, that code must be merged to the appropriate branch. The deployment then is made directly from that branch. ### Deployment is automated There are no human steps required, beyond the act of merging code to a new branch, to deploy changes to a new environment. Sometimes this is straightforward; sometimes this requires work to create migration tooling to enable. ### Deployment does not cause user-facing downtime Mature analytical systems are constructed in a way to not impact users during deployments. ### Rollbacks are automated Rollbacks are not only possible, but are automated. Deployments will inevitably surface errors despite robust testing, and mature analytical systems need to plan for this outcome by having a rollback strategy. The best rollback strategy includes smoke tests and automated rollbacks. ### Developers choose the size of the change Developers can choose to deploy a one-line change or a massive refactor to the entire system. The analytical system must not constrain the way in which they structure their patches. ## Operate and Observe Every analytical system has a production environment, and every organization has certain requirements for its production analytical environment: uptime, latency, throughput, correctness, etc. In the Operate and Observe phase we are _not_ making changes to the system; having deployed and validated changes in the prior phase, we are now operating it in steady state and observing its characteristics to validate that it is conforming to expectations. Best practices include: ### Always-on In the past, analytical systems were frequently unavailable for significant chunks of the day as new data was loaded or jobs were processed. This is no longer acceptable—analytical systems’ production environments should be assumed to be available 24x7x365, with modest windows for planned maintenance. ### Tolerate and recover from failure Your analytical system, with its thousands of models and dashboards and notebooks and sources, each containing thousands to billions of rows, will never be without errors. The goal is to be robust to these errors, not prevent them entirely. Build analytical assets that can recover from failure quickly and with minimal manual intervention. ### Catch errors before customers do Given that any mature analytical system will always contain errors, two of the most important metrics to measure are _time to detect_ an error and _time to resolve_ an error. The goal of error detection and remediation is to identify and resolve errors before your customers see them. This bar is very rarely hit inside of organizations today. Doing so requires mature processes around incident identification, triaging, and resolution, high quality instrumentation, clear component ownership, and around-the-clock on-call rotations. ### Test in production From [increment.com](https://increment.com/testing/i-test-in-production/): “Once you deploy, you aren’t testing code anymore, you’re testing systems—complex systems made up of users, code, environment, infrastructure, and a point in time. These systems have unpredictable interactions, lack any sane ordering, and develop emergent properties which perpetually and eternally defy your ability to deterministically test.” Said another way, most of the bugs you find are not “errors,” they are a mismatch between your business logic and the real world. You can never fully anticipate the real world in your test environment, so you inevitably need to test in prod. This requires both excellent instrumentation and tooling that allows you to explore this instrumentation data in real-time. ### Choose your own metrics, and then measure them religiously There are many metrics to choose from in the literature on observability: availability, uptime, latency, throughput, etc. These metrics are all in tension with one another, and there is no universal answer to how these tradeoffs should be managed. Every organization needs to understand these metrics, set their relative priorities, and set goals around the ones that matter. Missing those goals should generate action. ### Don’t overshoot Every additional 9 on your SLAs costs an order of magnitude of additional effort/resources to deliver. Assess the real business value of your system's characteristics and aim to deliver what is actually required. ## Discover and Analyze The Discover and Analyze phase includes two distinct but intertwined user flows: discovery of existing data artifacts (data sets, dashboards, metrics, etc.) and using those data assets to answer questions. This phase is where business value is ultimately created. Reports and dashboards are created and viewed. Exploratory data analysis is conducted. Hypotheses are tested. Causal relationships are investigated. Predictions are made. We find that this phase of the ADLC is, in practice, often relatively immature today. Every analytical question starts as research. An analyst sets out with a question about the business and looks for data to bring to bear on it. The analyst finds some data, hacks together some code or scripts or _whatever_ to do some sanity checks, and eventually starts to be convinced that there’s signal in the data. At that point, the analyst gradually starts to flip from a mindset of “I need to convince myself” to “I need to convince others.” At this point, the analyst anticipates a bunch of follow-on questions that might disconfirm their earlier conclusions and then proactively answers those. Assuming their initial conclusion stands up to this effort at disconfirmation, they eventually switch from “I need to convince others” to “I need to memorialize this insight.” At this point, they consolidate all the analytical artifacts that they have built to get them to this point, clean them, document them, and ship them. At that point, those become long-lived artifacts of the analytical system of an organization. This, then, is the core tension in the Discover and Analyze phase: **the same set of tools that promotes experimentation and exploration must then _also_ support maturity and productionization**. In practice, most tooling in the presentation layer does not enable this, and as a result this layer of the analytical system often completely skips the ADLC. This results in final products (reports, dashboards, notebooks, etc.) that are low-maturity, even if they are built on top of mature datasets. Errors can get introduced at any layer of the analytical system, and the presentation layer is no exception. Presentation layer artifacts _must_ go through the full ADLC before they become load-bearing in an organization. The process of productionizing a dashboard should be thought of as no less critical than, and fundamentally_ no different than_ productionizing a data pipeline. **This is why the ADLC is a loop.** As the analyst moves from exploratory data analysis to memorializing an insight for a wider audience, they shift from the Discover and Analyze phase to the Plan and Develop phase, and thus through another iteration of the entire process. ### Requirements of the discover and analyze phase Beyond the above, the ADLC does not believe that there is a ‘right’ or a ‘wrong’ way to conduct exploratory data analysis. Rather, it specifies a set of requirements that all users should have of their analytical systems in the Discover and Analyze phase: - Users should be able to **discover **the artifacts from a mature analytical system directly, through a single search bar, without having to go through any intermediary gatekeepers. - Users should always be able to **operate on data where they find it**, without passing it from person to person in informal networks or ever downloading it locally. - Users should be able to **leave feedback** on any element of a mature analytical system. This feedback should both lead to better discovery as well as fed back into the Plan phase. - Users should be able to straightforwardly **request the access that they need** from a mature analytical system to get their jobs done. - Users should be able to **delegate their own access** to a mature analytical system to their chosen tools and agents. - Users should be able to straightforwardly **validate the correctness and timeliness** of data from a mature analytical system. - Users should be able to straightforwardly **investigate the provenance** of any data element in a mature analytical system. - Users should be able to **view a history** of all state changes to a mature analytical system. - Users should be able to **choose the environment** of a mature analytical system they interact with: dev, staging, prod, etc. Finally, users should be able to **ignore the implementation details** of a mature analytical system. The system should _just work_ without these users needing to know all of the underlying technical details of _how _it works. ## Conclusion Our goal in publishing this paper is to create a consistent, shared framework for a mature analytics workflow: the ADLC. Many parts of this framework can be implemented today. Some require better tooling to effectively implement them. As such, the effort of building towards a mature analytics practice requires the entire industry—data practitioners and technology vendors—working together towards a shared vision of the future. This process will not play out overnight. Software engineering has only reached its current state of relative maturity after many decades of progress. It will take just as long in data. As an industry, we are immature in so many ways, and this paper only attempts to lay out, in the broadest of strokes, the path towards a solution. What is required now is to collectively roll up our sleeves and push the conversation forward in every single arena. We look forward to doing that work right alongside you. --- --- title: "29 ways to optimize costs in data pipelines, workflows & analyses" description: "Tactics to cut costs, boost efficiency, and free up your team for high-impact work—without overhauling your data pipelines." url: "https://www.getdbt.com/resources/29-ways-to-optimize-costs-in-data-pipelines-workflows-and-analyses" date: "2025-04-29" categories: ["Guide"] --- # 29 ways to optimize costs in data pipelines, workflows & analyses Tactics to cut costs, boost efficiency, and free up your team for high-impact work—without overhauling your data pipelines. When it comes to optimizing costs in the data workflow, there is no one-size-fits-all solution. The reality is that working with data can be expensive. But it doesn’t have to be inefficient nor should it require large overhauls of process and technology. In fact, there are also smaller, tactical improvements data teams can make that add up to massive savings over time. In this playbook, dbt Labs—along with our partners [Data Clymer](https://dataclymer.com/), [phData](https://www.phdata.io/), [BDO Digital](https://www.bdodigital.com/), and [Hakkoda](https://hakkoda.io/)—share tips for reducing costs in your data work. Some ideas are specific to dbt, but many can be adopted across multiple platforms. The tactics in this post are organized in three main categories: - How to optimize data platforms for efficiency and cost - Ways to optimize data development for cost - How to prioritize high-value work Some of these tactics are specific to dbt, but many can be adopted across multiple platforms. Some of these strategies are technical and others are cultural. But ultimately, all 29 of these are "no brainers". ## How to optimize data platforms for efficiency and cost ### 1. Measure the cost of your data workflows To really optimize the costs of your data workflows, you need to start by accurately measuring them. (You can’t gauge improvements if you don’t have a baseline.) Leverage Snowflake’s [resource monitors](https://docs.snowflake.com/en/user-guide/resource-monitors) and [system views](https://docs.snowflake.com/en/sql-reference/account-usage/metering_history) to actively monitor and report on credit consumption. BI tools such as [Sigma Computing](https://www.sigmacomputing.com/) provide out-of-the-box templates, enabling you to quickly understand how much you are spending on tools like [Fivetran](https://www.sigmacomputing.com/interactive-demos/fivetran-monitoring-template) and [Snowflake](https://www.sigmacomputing.com/interactive-demos/snowflake-cost-monitoring-template) with minimal time investment from your team. dbt offers [run time monitoring](https://docs.getdbt.com/docs/deploy/run-visibility) to provide insight into which models are taking the longest, giving you further insight into which part of your DAG can be optimized. These are just a few of the ways you can get a sense of existing costs, but be sure to check all the tools in your data stack to see which insights they can offer. - [_Data Clymer_](https://dataclymer.com/) ### 2. Conduct a model timing analysis Once you have a sense of existing costs, it’s time to dig into your model runs. Are any runs holding up other work? Is there a model that’s running way longer than expected? Excessive runtime can translate into large compute expenses in your cloud data platform—and thus opportunities for cost savings. To get a handle on model runs, you’ll need to conduct a model timing analysis. If you’re using dbt, you can simply use the [Model Timing tab](https://docs.getdbt.com/docs/deploy/run-visibility#model-timing) to inspect logs and identify areas for tuning. In fact, the dbt Labs team used this feature to [shave 90 minutes off our longest running model](https://docs.getdbt.com/blog/how-we-shaved-90-minutes-off-model)—which translated to $1800/month saved in Snowflake credits. The new dbt Explorer also provides metadata on dbt runs, such as [the models with the longest execution time](https://docs.getdbt.com/docs/collaborate/model-performance). You can use this information to assess trends in execution times and select areas for improvement. Regardless of how you conduct your model timing analysis, it’s a best practice to regularly inspect your model runs. By understanding the holistic data environment, targeted optimizations can be made, thereby enhancing performance and reducing costs. - [_BDO Digital_](https://www.bdodigital.com/) ### 3. Examine deployments Similar to model runs, you should examine how your deployments are running and make sure that you take advantage of the advanced selection/exclusion [methods](https://docs.getdbt.com/reference/node-selection/methods) built into dbt (state selection, source freshness selection, tag-based selection, etc). At the same time, examine for any models that can be consolidated into a singular data model to reduce redundancy and duplication. These two methods should always be the first levers you pull on to optimize performance and reduce compute and dbt runtime within your cloud data platform, as these tend to be the most straightforward way to optimize your pipeline. - [_phData_](https://www.phdata.io/) ### 4. Socialize metrics Next, integrate all these insights into your data governance processes. It’s important that data team members actually take the time to review trends and act upon them. Include cost reporting in your weekly or monthly standup meetings to socialize these metrics and ensure your team understands spending on the data platform and how they can help reduce these costs. Cost optimization isn’t a one-person job—it takes the full team. By incorporating these metrics into regular reporting, you ensure that each team member is aware of the levers they can pull to help reduce costs. - [_Data Clymer_](https://dataclymer.com/) ### 5. Use sampling for non-production environments Have you heard of the four V’s of data? It’s **velocity**, **veracity**, **volume**, and **variety**. You can look at cost improvements through the lens of the four V’s. For example, take a look at long-running development and CI/CD processes. These could be significant signs that you’re working with a lot of data that takes a while to develop and test, which can be resolved by working off of a sample of your data set, which could be implemented in a macro like: ![Example of a dbt Jinja macro that conditionally applies a sample clause for non-production environments, using a customizable sample type and size.](https://cdn.sanity.io/images/wl0ndo6t/main/7be9deb79a7b6d0a30de586f8add654fd490b76a-626x114.png) This sample macro can be applied to all data sets, allowing for development and CI/CD pipelines to run with a fraction of the data, helping reduce compute and storage costs. This same sort of solution can also be used to help optimize tests that run on extremely large datasets, using the where config and overloading the _get_where_subquery_ macro to look at certain running partitions of your data using something like: ![Example of a dbt YAML config and custom Jinja macro that dynamically filters data using a rolling date window placeholder (__window__) in a model-level uniqueness test.](https://cdn.sanity.io/images/wl0ndo6t/main/e3b6ffd362250e07334b55509ffdfc726bf6bb86-626x542.png) By reducing processing time and storage volumes in non-production environments, these levers provide ways to start optimizing datasets based on the needs of the four V’s for our data. - [_phData_](https://www.phdata.io/) ### 7. Defer to production with dbt Defer to Production is [a handy feature in dbt ](https://docs.getdbt.com/docs/cloud/about-cloud-develop-defer) that reduces model builds in dev environments, reduces build times for extremely active developers, and removes the need to import lots of data into the dev environment. You can use [defer to production in a few ways:](https://www.getdbt.com/blog/optimize-costs-with-dbt-cloud-defer-to-production-feature) - With the [dbt CLI](https://docs.getdbt.com/docs/cloud/cloud-cli-installation), it’s automatically enabled for all commands. - In the [dbt IDE](https://docs.getdbt.com/docs/cloud/dbt-cloud-ide/develop-in-the-cloud), you can simply toggle a button at the bottom right of the editor. Defer to production ensures that when developers edit models, they can build, run, and test them in a development environment, without needing to run and build all the upstream models that came before them. Instead, dbt will automatically fetch the data for the non-edited models from the production environment, saving storage and compute costs that would come from rebuilding models that don’t need to be rebuilt. - _dbt Labs_ ### 8. Manage keys in cloud data platforms Managing cluster keys in Snowflake, partition keys in BigQuery, and sort/dist keys in Redshift is another way to effectively manage data platform compute costs. These keys play a pivotal role in determining how data is distributed and organized within warehouse tables, directly impacting query performance and resource utilization. To start, strategically choose and fine-tune these keys to minimize the amount of data that needs to be processed during queries, reducing query execution times and, in turn, the associated costs. dbt Labs offers a native configuration to manage these keys directly in your dbt project. For more information, see the platform-specific configs for [BigQuery](https://docs.getdbt.com/reference/resource-configs/bigquery-configs#using-table-partitioning-and-clustering), [Snowflake](https://docs.getdbt.com/reference/resource-configs/snowflake-configs#using-cluster_by), and [Redshift](https://docs.getdbt.com/reference/resource-configs/redshift-configs#using-sortkey-and-distkey). Keep in mind that **managing these keys does usually incur additional costs**. So be sure to do careful analysis on query usage patterns to ensure the performance gains outweigh the cost associated with managing the keys. - [_Data Clymer_](https://dataclymer.com/) ### 9. Test only the relevant changes It goes without saying that you shouldn’t re-run a full pipeline or dataset to test a small change. But, it happens. And when it happens, it’s expensive. To avoid running more data than you need to (and incurring the related data platform costs) make sure to incorporate these practices into your team’s routine: - Enable and disable parts of the pipeline to test discrete units. - [Use Slim CI in dbt](https://docs.getdbt.com/best-practices/best-practice-workflows#run-only-modified-models-to-test-changes-slim-ci) to run and test only modified models. - Implement a write-audit-publish style deployment where a pipeline's particular results can be verified in a non-production environment and, once approved, [swapped](https://docs.snowflake.com/en/sql-reference/sql/alter-table) into production. - Use smaller windows for data testing on extremely large datasets with long-running tests. - [_phData_](https://www.phdata.io/) _& dbt Labs_ ### 10. Auto-cancel stale CI builds Similarly, if you’re using dbt, you can also use the [dbt scheduler](https://docs.getdbt.com/docs/deploy/job-scheduler#run-cancellation-for-over-scheduled-jobs) to manage the auto-cancellation of stale CI builds. With this feature, dbt can detect and cancel any in-flight, now-stale checks in favor of executing tests on the newest code committed to a PR. Auto-cancellation improves team productivity while reducing data platform spending on wasteful CI runs. - _dbt Labs_ ### 11. Centralize the code database It’s not uncommon for data teams to learn that legacy code is still getting computed by the cloud data platform many years after it was written. Sometimes it turns out that no one on the current team has context for what this code accomplishes. In these scenarios, it’s essential to centralize the code database to get a stronger grasp on exactly what code is getting executed and why. One team that undertook such an effort was the data team at Total Wine—they [dug into their code database](https://www.getdbt.com/blog/4-experts-on-how-to-optimize-costs-in-your-data-work-pipelines) to make sure they understood every script and metric that was getting computed every night. “We saw that certain old pieces of code were getting re-computed or re-recalculated, multiple kinds across several tables,” said Pratik Vij, senior manager of data engineering at Total Wine. “This core duplication was not only expensive but there was no added benefit of recomputing these values because the underlying data wasn't changing.” These findings prompted Total Wine to centralize their code database. Once they did, Pratik noted, “We were able to save around 10 to 15 percent” on data platform costs. - [_Total Wine_](https://www.getdbt.com/resources/optimizing-costs-in-your-data-workflow) ## Ways to optimize data development for cost ### 12. Establish quality foundations by providing transparency to data consumers There are many tactics that help save time, resources, effort—and thus money—in the data development process. Ultimately, these tactics boil down to the same key idea: The team must write great code and build strong pipelines, which guarantees long-term stability. To this end, teams must establish quality foundations early. You can incorporate quality metrics and data consumer feedback mechanisms that bolster the robustness of your data pipelines. For example, In dbt, you can leverage native support for in-lineage [tests](https://docs.getdbt.com/docs/build/data-tests), [continuous integration](https://docs.getdbt.com/docs/deploy/continuous-integration), and [documentation](https://www.getdbt.com/blog/navigate-and-understand-your-dbt-cloud-projects-with-dbt-explorer). Put another way: In some situations, data engineering can feel like a black box to the rest of the organization. Business stakeholders submit requests and hope the data model is built correctly. But too often, data products are met with the response of, “Oh that’s wrong, you have to retest things and go back.” By leveraging tools and features that provide clarity and transparency into the data development process, data teams can get feedback sooner and ensure that necessary corrections are made early on in the process. - [_BDO Digital_](https://www.bdodigital.com/) ### 13. Add comment blocks for context Every engineer knows the pain of debugging old code without comments. Logic that may have made sense to a prior team can look like “alphabet soup” years later. Improving that code and figuring out how it impacts current work can take days of manual work—and that’s expensive time that could be better spent on other projects. Adding comment blocks for context should be a regular part of your data development process. Make sure to consistently include comment blocks that give context about the "what," "why," and "how" of data models. It’ll save you and your team headaches—and critical resources—in the future. - [_Hakkoda_](https://hakkoda.io/) ### 14. Reconfigure SQL logic to be more efficient “We ended up reducing our monthly data platform spend by a little over 50%” [just by reconfiguring SQL query logic to be more efficient](https://www.getdbt.com/blog/4-experts-on-how-to-optimize-costs-in-your-data-work-pipelines), Mike Moyer, data engineer at Paxos said. His team noted that simple improvements like changing a sum to be incremental rather than fully recalculated had a huge impact when extended to tens of thousands of rows every time the model is run. The Paxos team’s advice: Start optimization early and look for big wins first. The faster you get started, the sooner these savings kick into gear. Even if an optimization is seemingly obvious or unsophisticated, taking the time to improve these basic efficiencies can produce significant savings. - [_Paxos_](https://www.getdbt.com/resources/optimizing-costs-in-your-data-workflow) ### 15. Examine materialization types If you’re using dbt for transformation, you’ll be aware that [materializations](https://docs.getdbt.com/docs/build/materializations) are strategies for persisting dbt models in a cloud data platform. By default, dbt models are materialized as “views” but can also be built as tables, incremental models, ephemeral models, and materialized views. Custom materializations are available as well. To save on data development costs, be sure to [examine the type of materializations you’ve created](https://www.phdata.io/blog/dbt-materialization-types-and-strategies-explained/). Remember, stacked views can take a long time to query, so with large volumes of data, it is often best to build data sets incrementally. - [_phData_](https://www.phdata.io/) ### 16. Embrace a modular intermediate layer [Modular data modeling](https://www.getdbt.com/analytics-engineering/modular-data-modeling-technique) The transition to a modular intermediate layer can mark a turning point in cost efficiency, and it’s easy to facilitate with dbt. For example, a BDO Digital client moved their upstream application stack from a monolithic architecture to a microservices architecture, but they were able to plan ahead by implementing dbt with a modular design even while using it with a monolithic architecture. This meant that when the team was ready to fully move their application stack to a microservices architecture, dbt was ready to adapt. Due to dbt's resilience for upstream changes, the team was able to avoid expensive data platform refactoring. - [_BDO Digital_](https://www.bdodigital.com/) ### 17. Prioritize meaningful data quality tests It’s essential to leverage tests for data quality. But be sure your team knows what they’re testing and why. In your data transformation tool (like [dbt](https://www.getdbt.com/product/dbt)), emphasize impactful data quality tests over meeting arbitrary coverage targets. By eliminating superfluous tests, execution costs are reduced. Meanwhile, effective tests build data consumer trust and facilitate more confident, quicker development cycles. - [_BDO Digital_](https://www.bdodigital.com/) ### 18. Set custom rules for SQL formatting Code style isn’t about coding with _style_. It’s about enhancing your team’s development workflow with legible, reliable syntax. Tools like linters and formats can automate much of this work by analyzing code for errors, bugs, and style and formatting issues. In dbt, you can also [customize linting rules](https://docs.getdbt.com/docs/cloud/dbt-cloud-ide/lint-format#customize-linting) directly in the IDE. By setting custom SQL formatting rules, you ensure that your team writes code in a clean and consistent manner—with less time spent on simple syntax mistakes. - _dbt Labs_ ### 19. Use the dbt Clone command The [dbt clone command](https://docs.getdbt.com/blog/to-defer-or-to-clone) allows you to inexpensively copy schema structures using zero-copy cloning. It facilitates safe modifications, avoids data drift, and supports dynamic sources. You can use dbt clone for creating sandboxes, making data accessible in BI tools, and ensuring smooth blue-green deployments. Ultimately, it's a valuable tool for cost-effective, flexible, and efficient data workflows. - _dbt Labs_ ### 20. Rerun errored jobs from point of failure When a job fails, it's typically due to a specific issue or error within a portion of the data transformation process. By rerunning the job from the point of failure, you can skip re-executing the successfully completed steps, thus conserving resources and avoiding unnecessary reprocessing of data. Of course, you can [use dbt](https://docs.getdbt.com/docs/deploy/retry-jobs) to easily identify any failed steps and examine the error message to implement a fix. Rerunning the job from the point of error, instead of from scratch, also saves the team time and leads to a faster resolution of issues. - _dbt Labs_ ## How to prioritize high-value work ### 21. Run concurrent CI checks With dbt, [CI runs can execute concurrently](https://docs.getdbt.com/docs/deploy/continuous-integration#concurrent-ci-checks). This means you can reduce overall compute time with dbt’s ability to intelligently understand dependencies and run select jobs in parallel. This also saves the team time, as teammates never have to wait to get a CI check review. - _dbt Labs_ ### 22. Use dashboard tiles to verify data quality The work of a data team doesn’t exist in a silo. It’s often leveraged by downstream consumers in tools like BI platforms to make sense of data and make key decisions. Between the time when the data is modeled and when it’s queried in another platform, though, questions can arise about the freshness and quality of that data. And if decisions are made based on outdated data, you can bet there will be more work down the road to correct those mistakes. To avoid such inefficiencies, make sure your data consumers have “sanity checks” they can reference in their downstream platforms. With dbt, for instance, you can embed [dashboard status tiles](https://docs.getdbt.com/docs/deploy/dashboard-status-tiles) in downstream platforms that show whether data in that dashboard has passed quality and freshness criteria. If any data freshness check fails, the dashboard status tile will alert a consumer that the data may be stale. - _dbt Labs_ ### 23. Standardize on methods that scale Trendy tools come and go and data development methods are constantly advancing. As the industry changes and more roles get involved in data modeling or analysis, it’s important to make sure your team is centered around practices that scale. SQL and Python, for instance, are the two most popular coding languages for data analysis. They aren’t going away. But some data development tools use GUI (graphical user interface) methods for data modeling, which require users to learn an analysis method specific to that tool. Over time, these niche tools and methods become outdated, which can lead to vendor lock-in on archaic tools or the proliferation of tribal knowledge (only a few people on the team know how to work the tool) which slows down the rest of the organization. Set your team up to move fast now and in the future by selecting tools built for scale, adaptability, and flexibility. Additionally, train users (data producers and consumers alike) on standard and widely used languages like SQL and Python. If you use a data platform that the whole team can standardize on, you’ll also save countless hours of confusion about which data logic or metric is correct. With a unified platform, everyone has insight into which data models are accurate, and questions about the data are easy to debug. - _dbt Labs_ ### 24. Strategically allocate workflows Focus on defining 2-3 key platform objectives (like cost, quality, consumer trust, security, development velocity, or self-service capabilities) and align design choices accordingly. For example, prioritizing optimizations for a cloud data platform and streamlined pipeline design led a client of BDO Digital to see a dramatic reduction in execution times. This approach ensures effective trade-offs are consistently made, as keeping these objectives top of mind helps the team focus on what matters, rather than trying to tackle everything at once. It’s an especially helpful strategy for large or legacy organizations that may have more data complexity to deal with. - [_BDO Digital_](https://www.bdodigital.com/) ### 25. Reference and re-use code There’s no sense in rewriting code from scratch when you could just reference or re-use existing code. For data transformation, dbt Labs provides [a hub of community packages](https://hub.getdbt.com/) that developers can use. This way, teams spend more time focusing on their unique business logic, and less time implementing code that someone else has already spent time perfecting. In addition, [dbt Catalog](https://www.getdbt.com/product/dbt-catalog) makes it easy to reference existing business logic in dbt. This means that if someone on your team has already built and validated data assets, the rest of the team can easily discover and reference those assets, instead of having to rebuild them. - _dbt Labs_ ### 26. Automate documentation Documentation is a necessary part of data development—but the manual effort required to create and maintain documentation can be burdensome. Luckily, there are ways to automate code documentation. For example, dbt will automatically generate documentation for data models, tests, and transformations based on comments and descriptions in your SQL code. The documentation is stored centrally within dbt, making it easily accessible to all team members. This central repository also ensures that the documentation is version-controlled and aligned with the latest code changes, fostering accuracy and reliability. This automation not only saves time for data teams but also promotes better collaboration and understanding of the data transformations within the organization—which ensures teams can move faster in the future. - _dbt Labs_ ### 27. Use AI to take the load off manual work The capabilities for AI in data work are already strong and growing by the day. The opportunities to quickly diagnose errors, write better code, developer software faster, and analyze patterns seem endless. Of course, it’s necessary to include human checks for accuracy and make sure AI isn’t exposed to company secrets. In addition, training and deploying AI models can incur significant computational costs, especially when working with large datasets or complex models. At the same time, there are ways to leverage AI to improve data team productivity at scale. For example, the data team at Sharp Healthcare [used AI to accelerate a transition process](https://www.getdbt.com/blog/4-experts-on-how-to-optimize-costs-in-your-data-work-pipelines) that would have been onerous and time-consuming to do by hand. They needed to convert 15,000 SQL server views into dbt models, but each conversion came with some CTEs and Snowflake syntax that needed to get taken care of. So, they looked into how AI could help. “We took about three weeks massaging prompts and everything else and standing up a pipeline where we can go through all those views, and then generate the corresponding dbt.sql file,” said Clay Townsend, director of analytics at Sharp Healthcare. “It was almost like having a data engineer intern there as part of the pipeline. It's been great.” - [_Sharp Healthcare_](https://www.getdbt.com/resources/optimizing-costs-in-your-data-workflow) ### 28. Schedule runs and tests It takes time for data to run. But that time doesn’t have to overlap with typical work hours. [Schedule model runs and tests](https://docs.getdbt.com/docs/deploy/job-scheduler) so that data transformations can be processed automatically and at specified intervals. Automation saves time by reducing the need for manual intervention in running data transformations. It also allows teams to schedule jobs to run during non-business hours, which optimizes resource usage and ensures that data is ready for analysis when it’s needed at the start of the next work day. - _dbt Labs_ ### 29. Up-skill data consumers (with guardrails in place) The most expensive resource a data team has? Engineering time. In an ideal organization, engineers spend their time focused on deeper problems that only they can solve. But in reality, engineers at many organizations often get swamped in manual tasks, infrastructure maintenance, responding to endless ticket requests, and debugging issues that could have been avoided in the first place. Many of the strategies included in this list will help protect engineering time by establishing quality data foundations early on. However, it’s important to think about the self-service aspect of data analysis too. Data doesn’t just sit with the engineering team—it’s used by teams throughout the organization to make key business decisions. Training and enabling data consumers to analyze—and in some cases even model—that data themselves will ensure other teams can get their answers sooner without requiring engineering support. With more people fully ramped on how to use data tools, the organization can operate more efficiently. Of course, it goes without saying that [strong guardrails are necessary for any initiative like this](https://www.getdbt.com/blog/managing-data-democratization). But with proper governance measures in place, more people can understand and leverage data—saving time for every team and allowing engineers to focus on what matters most. - _dbt Labs_ --- --- title: "How to do analytics at scale: 10 tips from data leaders" description: "See actionable insights from data leaders on scaling analytics with transparency, team alignment, and the right tools." url: "https://www.getdbt.com/resources/data-leaders-analytics-at-scale" date: "2024-12-13" categories: ["eBook"] --- # How to do analytics at scale: 10 tips from data leaders See actionable insights from data leaders on scaling analytics with transparency, team alignment, and the right tools. #### Want to learn how to scale analytics like a pro? Data is the lifeblood of your organization, but scaling analytics across teams, technologies, and business units is no easy feat. We've created a free guide for data leaders, from data leaders. **“How to do analytics at scale: 10 tips from data leaders”** is your roadmap to building an adaptable, enterprise-wide data strategy. Inside, you'll find proven strategies from seasoned data leaders at companies like Roche, Nasdaq, and SurveyMonkey, how to decipher which tools matter most and how to avoid costly missteps, how to foster collaboration and empowerment across teams while aligning on data-driven goals, and how to devise scalable solutions to future-proof your analytics. #### **What else is inside?** - 10 expert tips to overcome common pitfalls - Insights on embedding data teams within business units - Practical advice for leveraging transparency and data literacy - A roadmap for adopting transformative tools like dbt It isn’t just about technology—it’s about people, processes, and driving your business objectives with a strong data strategy. #### **Ready to see what all the hype's about?** Join the thousands of organizations using dbt to build smarter workflows. Download "How to do analytics at scale: 10 tips from data leaders" today to transform your organization and bring data-driven decisions to life. [Watch video](https://youtu.be/sFuzN5XqSqo?si=MxkkIx2eqhCuwLuG) --- --- title: "Unlocking the power of AI with dbt Cloud" description: "Learn how dbt ensures trustworthy AI through governance, data quality, and semantic modeling for scalable insights." url: "https://www.getdbt.com/resources/unlocking-the-power-of-AI-with-dbt-Cloud" date: "2024-06-26" categories: ["Guide"] --- # Unlocking the power of AI with dbt Cloud Learn how dbt ensures trustworthy AI through governance, data quality, and semantic modeling for scalable insights. **Explore AI’s role in modern data management** Learn how leading companies are using AI to enhance productivity and decision-making through dbt Cloud. This white paper provides an in-depth look at AI’s impact on data teams and the solutions dbt Cloud offers. **What you’ll learn:** - **AI’s impact on data teams**: Discover how AI is reshaping the responsibilities and workflows of data teams. - **Overcoming AI deployment challenges**: Understand the common challenges in integrating AI and how dbt Cloud simplifies these complexities. - **Tools for ensuring data integrity**: Explore dbt Cloud’s features that help maintain high-quality, secure data for AI applications. --- --- title: "A guide to data mesh" description: "Download our guide on the four principles of data mesh to build your own mesh architecture" url: "https://www.getdbt.com/resources/a-guide-to-data-mesh-download" date: "2024-04-15" categories: ["Guide"] --- # A guide to data mesh Download our guide on the four principles of data mesh to build your own mesh architecture Read our guide on the four principles of data mesh to build your own mesh architecture. Dive into: - What is a data mesh, and why do organizations adopt one? - The four principles of data mesh—domain-oriented data, data products, self-service, and federated governance—and how to implement them - How dbt can help --- --- title: "dbt Cloud: The control plane for data collaboration at scale" description: "Learn more about the various platform features of dbt Cloud and how it serves as a control plane for data collaboration at scale." url: "https://www.getdbt.com/resources/whitepaper-the-control-plane-for-data-collaboration-at-scale" date: "2024-04-12" categories: ["eBook"] --- # dbt Cloud: The control plane for data collaboration at scale Learn more about the various platform features of dbt Cloud and how it serves as a control plane for data collaboration at scale. dbt has quickly become an industry standard for transformation workflows. But as the amount of data and number of data consumers scale within an organization, so does data complexity, which can impact data quality, velocity, and costs. Using dbt Cloud, data teams have a standardized way to write data transformation logic, but also a control plane to govern, catalog, orchestrate, and monitor their end-to-end data pipelines to foster data trust and collaboration at scale. Learn more about how dbt Cloud serves as your control plane for data. --- --- title: "Manage data complexity at scale" description: "Learn how to scale data workflows, build trust, and cut insight costs as complexity grows—straight from analytics leaders." url: "https://www.getdbt.com/resources/manage-data-complexity-at-scale-ebook" date: "2024-02-20" categories: ["Report"] --- # Manage data complexity at scale Learn how to scale data workflows, build trust, and cut insight costs as complexity grows—straight from analytics leaders. As your data grows, so does the demand for it. But at many businesses, access to data and speed-to-insight comes at the cost of quality. Data should be a competitive advantage, but without the right set of tools and practices to manage the growing complexity, data teams get caught trying to debug errors, decipher messy code, and respond to endless ticket requests.It doesn’t have to be this way. With dbt Cloud, data leaders can spearhead a unified approach for working with data that ensures quality and consistency. **This eBook explains exactly how businesses can:** - Build trust in data and data teams - Ship data products faster - And reduce the cost of producing insight You don’t have to sacrifice agility for quality. Download the _Manage Data Complexity at Scale_ eBook to learn more. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/1b2b60e782f3e735e27d6f7ebf33fc6e08a7bb23-2878x1618.png) --- --- title: "How Fivetran and dbt help with ELT" description: "Learn how Fivetran and dbt streamline ELT processes, automating data pipelines for faster, more efficient analytics." url: "https://www.getdbt.com/resources/how-fivetran-and-dbt-help-with-elt" date: "2024-02-20" categories: ["Guide"] --- # How Fivetran and dbt help with ELT Learn how Fivetran and dbt streamline ELT processes, automating data pipelines for faster, more efficient analytics. As cloud data platforms have grown and evolved over the years, “ELT” (as compared to legacy “ETL”) has emerged as a popular approach to data integration and transformation in modern environments. Both Fivetran and dbt are at the center of this paradigm shift. ##### Inside this guide you will learn: - How and why ELT has emerged as a popular approach to data integration and transformation - Opportunities and challenges with modernizing data pipelines - How Fivetran and dbt help organizations streamline and simplify data pipelines - The benefits of using Fivetran and dbt for ELT ##### Created by: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/4a493d1387b9254980cf4ea4bad33b98a6e77d3f-788x98.png) --- --- title: "Accelerators for cloud data platform transition" description: "Accelerate your cloud data platform transition with dbt Cloud. Build pipelines, empower teams, and adapt governance effectively." url: "https://www.getdbt.com/resources/accelerators-for-cloud-data-platform-transition-guide" date: "2024-02-14" categories: ["Guide"] --- # Accelerators for cloud data platform transition Accelerate your cloud data platform transition with dbt Cloud. Build pipelines, empower teams, and adapt governance effectively. Moving to a cloud data platform is an exciting decision, but can also be a challenging project for larger organizations. Gaining stakeholder alignment, considering team redesign, and strategizing new data management paradigms aren’t flip-of-the-switch events. dbt Labs has helped hundreds of enterprise organizations accelerate their move to a more modern approach to analytics, without losing momentum. We believe the following field-tested approach will make a meaningful difference in reducing the burden of your own transition. **Inside this guide you will learn how to:** - How to build a data delivery pipeline that matches your new speed of business - How to enable data developers and consumers to confidently self- serve insights - Why governance and management practices change through cloud migration, and how to respond --- --- title: "Building the business case for dbt Cloud" description: "Learn how to align dbt Cloud with business objectives, tackle common challenges, and migrate smoothly." url: "https://www.getdbt.com/resources/making-the-case-for-dbt-cloud" date: "2024-02-14" categories: ["Guide"] --- # Building the business case for dbt Cloud Learn how to align dbt Cloud with business objectives, tackle common challenges, and migrate smoothly. Get real-world strategies from dbt Cloud users, insights from the latest State of Analytics Engineering report, and expert advice to help you successfully adopt and scale dbt Cloud. Discover how dbt Cloud can speed data delivery up to 20x, reducing inconsistencies by up to 80%, and establish trust across your organization with centralized, actionable insights. #### **What’s inside?** - Templates to track success and evaluate impact - Step-by-step guidance for a seamless rollout - Frameworks to align data initiatives with business goals (plus tools like the [TCO Calculator](https://www.getdbt.com/tco-calculator-confirm) to analyze your investment) #### **Why dbt Cloud?** We need more than just data; we need data we can trust, fast. dbt Cloud is your answer: a flexible, efficient platform that empowers everyone in your organization to transform and use data confidently. #### _Ready to make your case?_ Join the thousands of organizations using dbt Cloud to build smarter workflows—download the Business Case Guide today to transform your organization and bring data-driven decisions to life. --- --- title: "Achieve a 194% ROI with dbt" description: "Discover how dbt impacts ROI, boosting data governance, accessibility, and scalability for greater trust in your data.." url: "https://www.getdbt.com/resources/study-forrester-tei" date: "2024-02-13" categories: ["Report"] --- # Achieve a 194% ROI with dbt Discover how dbt impacts ROI, boosting data governance, accessibility, and scalability for greater trust in your data.. #### Download the Total Economic Impact™ study dbt Labs recently commissioned Forrester Consulting to conduct a Total Economic Impact™ study on the benefits of deploying dbt Cloud. It examined the potential return on investment a composite organization gained by investing in a more governed, accessible, and scalable approach to data transformations. ![ROI of dbt Cloud graphic](https://cdn.sanity.io/images/wl0ndo6t/main/bcacceae4aa662c9cc98f61c7ba5e0805e167e73-3184x2188.png) Download the study to learn how you can improve trust in your data by helping teams move beyond outdated stored procedures or drag-and-drop tools that are less transparent and approachable than modern approaches. --- --- title: "Demo dal vivo di dbt platform – Italia" description: "Guarda una demo dal vivo di un'ora sulla dbt platform" url: "https://www.getdbt.com/it/resources/webinars/demo-dal-vivo-di-dbt-platform-italia" date: "2026-08-17" categories: ["Live Demo"] --- # Demo dal vivo di dbt platform – Italia Guarda una demo dal vivo di un'ora sulla dbt platform ##### Progettata per agenti e analisi di cui ti puoi fidare 🗓️ martedì 29 settembre, ore 11:00 Vuoi sapere come dbt aiuta i team a creare, testare e distribuire pipeline di dati pronte per l'IA più velocemente? Partecipa a una demo dal vivo della dbt platform, pensata per i team di dati in Italia. Guarda una demo dal vivo di un'ora sulla dbt platform, con dbt Core v2.0, dbt Wizard, dbt State e l'intero ciclo di vita dell'analytics engineering in azione. Scoprirai come dbt aiuta i team di dati a lavorare più velocemente e con maggiore sicurezza — ottimizzando i costi dei dati sul cloud, accelerando lo sviluppo con IA progettata appositamente e costruendo la base governata alla base di analisi e IA affidabili. **Cosa imparerai:** ##### Scopri: - Come trovare, navigare e comprendere le risorse in dbt Catalog, inclusi il lineage del progetto, il lineage tra progetti e il lineage a livello di colonna - Come far emergere segnali sulla qualità dei dati ed exposure a valle su dashboard, applicazioni e pipeline di data science per valutare rapidamente qualità, utilizzo e impatto ##### Sviluppa: - Come sviluppare più velocemente con il motore dbt Fusion, utilizzando analisi statica basata sul compilatore per individuare gli errori prima dell'esecuzione, così se compila in sviluppo, funziona in produzione - Come sviluppare nel tuo ambiente preferito con dbt Studio IDE o l'estensione dbt per VS Code, con linting integrato, feedback in tempo reale e flussi di lavoro consapevoli del dialetto ##### Collabora: - Come scalare la collaborazione tra team con dbt Mesh, collegando progetti di proprietà dei singoli domini mantenendo chiari ownership, contratti e governance - Come allineare il lavoro di analytics attorno a metriche coerenti e governate, basate su dbt Semantic Layer ##### Opera: - Come pianificare e orchestrare le pipeline con scheduling dei job, logging e avvisi integrati - Come saltare esecuzioni di modelli non necessarie con dbt State, ricostruendo solo ciò che è cambiato per ridurre i costi di calcolo e accelerare la consegna - Come gestire e monitorare la spesa con Cost Insights ##### IA e agenti: - Come distribuire modelli più velocemente con dbt Wizard, un agente IA progettato appositamente per l'analytics engineering governato, che comprende il contesto del tuo progetto, il lineage, i test, i contratti e le metriche - Come costruire flussi di lavoro pronti per l'IA con il dbt MCP Server, offrendo agli agenti IA e agli strumenti automatizzati l'accesso ai metadati governati dei progetti dbt e alle azioni dbt supportate **Chi dovrebbe partecipare:** - Data practitioner - Data engineer - Analytics engineer - Solution architect - Giancarlo Gavotti --- --- title: "Démo en direct de la dbt platform – France" description: "Regardez une démonstration en direct d'une heure de la dbt platform" url: "https://www.getdbt.com/fr/resources/webinars/demo-en-direct-de-la-dbt-platform-france" date: "2026-08-17" categories: ["Live Demo"] --- # Démo en direct de la dbt platform – France Regardez une démonstration en direct d'une heure de la dbt platform ##### Conçue pour des agents et des analyses en qui vous pouvez avoir confiance 🗓️ **mardi 6 octobre, 11h00 CET** Vous voulez savoir comment dbt aide les équipes à créer, tester et déployer des pipelines de données prêtes pour l'IA plus rapidement ? Participez à une démonstration en direct de la dbt platform, conçue pour les équipes data en France. Regardez une démonstration en direct d'une heure de la dbt platform, incluant dbt Core v2.0, dbt Wizard, dbt State, et l'ensemble du cycle de vie de l'analytics engineering en action. Vous découvrirez comment dbt aide les équipes data à avancer plus vite et avec plus de confiance — en optimisant les coûts des données cloud, en accélérant le développement grâce à une IA conçue sur mesure, et en construisant le socle gouverné derrière des analyses et une IA fiables. **Ce que vous allez apprendre :** ##### Découvrir : - Comment trouver, naviguer et comprendre les ressources dans dbt Catalog, y compris le lineage de projet, le lineage inter-projets et le lineage au niveau des colonnes - Comment faire ressortir les signaux de qualité des données et les expositions en aval sur les tableaux de bord, les applications et les pipelines de data science pour évaluer rapidement la qualité, l'utilisation et l'impact ##### Développer : - Comment développer plus vite avec le moteur dbt Fusion, grâce à une analyse statique pilotée par le compilateur qui détecte les erreurs avant l'exécution, de sorte que si ça compile en dev, ça fonctionne en prod - Comment développer dans l'environnement de votre choix avec dbt Studio IDE ou l'extension dbt pour VS Code, avec linting intégré, retour en temps réel et flux de travail adaptés au dialecte SQL ##### Collaborer : - Comment faire évoluer la collaboration entre équipes avec dbt Mesh, en connectant des projets détenus par domaine tout en gardant une ownership, des contrats et une gouvernance clairs - Comment aligner le travail analytique autour de métriques cohérentes et gouvernées, propulsées par le dbt Semantic Layer ##### Exploiter : - Comment planifier et orchestrer des pipelines grâce à la planification de jobs, aux journaux et aux alertes intégrés - Comment éviter les exécutions de modèles inutiles avec dbt State, en ne reconstruisant que ce qui a changé pour réduire les coûts de calcul et accélérer la livraison - Comment gérer et suivre les dépenses avec Cost Insights ##### IA et agents : - Comment livrer des modèles plus rapidement avec dbt Wizard, un agent IA conçu spécifiquement pour l'analytics engineering gouverné, qui comprend le contexte de votre projet, le lineage, les tests, les contrats et les métriques - Comment construire des workflows prêts pour l'IA avec le dbt MCP Server, donnant aux agents IA et aux outils automatisés l'accès aux métadonnées gouvernées du projet dbt et aux actions dbt prises en charge **Qui devrait y assister :** - Data practitioners - Data engineers - Analytics engineers - Architectes de solutions - Brice Lepoutre --- --- title: "dbt platform live demo - South Africa" description: "Watch a one-hour walkthrough of the dbt platform" url: "https://www.getdbt.com/resources/webinars/dbt-platform-live-demo-south-africa" date: "2026-08-17" categories: ["Live Demo"] --- # dbt platform live demo - South Africa Watch a one-hour walkthrough of the dbt platform ##### Built for agents and analytics you trust 🗓️ Thursday October 1st, 12pm SAST Want to know how dbt helps teams build, test, and deploy AI-ready data pipelines faster? Join a live walkthrough of the dbt platform, built for data teams across South Africa. Watch a one-hour walkthrough of the dbt platform, including dbt Core v2.0, dbt Wizard, dbt State, and the full analytics engineering lifecycle in action. You'll see how dbt helps data teams move faster with more confidence — optimizing cloud data costs, accelerating development with purpose-built AI, and building the governed foundation behind trusted analytics and AI. **What you'll learn:** ##### Discover: - How to find, navigate, and understand resources in dbt Catalog, including project lineage, cross-project lineage, and column-level lineage - How to surface data health signals and downstream exposures across dashboards, applications, and data science pipelines to quickly assess quality, usage, and impact ##### Develop: - How to build faster with the dbt Fusion engine, using compiler-driven static analysis to catch errors before you run, so if it compiles in dev, it runs in prod - How to build in your preferred environment with dbt Studio IDE or the dbt VS Code extension, with built-in linting, live feedback, and dialect-aware development workflows ##### Collaborate: - How to scale collaboration across teams with dbt Mesh, connecting domain-owned projects while keeping ownership, contracts, and governance clear - How to align analytics work around consistent, governed metrics powered by the dbt Semantic Layer ##### Operate: - How to schedule and orchestrate pipelines with built-in job scheduling, logging, and alerting - How to skip unnecessary model runs with dbt State, rebuilding only what has changed to reduce compute costs and accelerate delivery - How to manage and observe spend with Cost Insights ##### AI and agents: - How to ship models faster with dbt Wizard, an AI agent purpose-built for governed analytics engineering that understands your project context, lineage, tests, contracts, and metrics - How to build AI-ready workflows with the dbt MCP Server, giving AI agents and automated tools access to governed dbt project metadata and supported dbt actions **Who should attend:** - Data practitioners - Data engineers - Analytics engineers - Solution architects - Ruth McElroy --- --- title: "dbt platform live demo - UAE" description: "Watch a one-hour walkthrough of the dbt platform" url: "https://www.getdbt.com/resources/webinars/dbt-platform-live-demo-uae" date: "2026-08-17" categories: ["Live Demo"] --- # dbt platform live demo - UAE Watch a one-hour walkthrough of the dbt platform ##### Built for agents and analytics you trust **🗓️ Thursday October 1st, 2pm GST** Want to know how dbt helps teams build, test, and deploy AI-ready data pipelines faster? Join a live walkthrough of the dbt platform, tailored for data teams across the UAE. Watch a one-hour walkthrough of the dbt platform, including dbt Core v2.0, dbt Wizard, dbt State, and the full analytics engineering lifecycle in action. You'll see how dbt helps data teams move faster with more confidence — optimizing cloud data costs, accelerating development with purpose-built AI, and building the governed foundation behind trusted analytics and AI. **What you'll learn:** ##### Discover: - How to find, navigate, and understand resources in dbt Catalog, including project lineage, cross-project lineage, and column-level lineage - How to surface data health signals and downstream exposures across dashboards, applications, and data science pipelines to quickly assess quality, usage, and impact ##### Develop: - How to build faster with the dbt Fusion engine, using compiler-driven static analysis to catch errors before you run, so if it compiles in dev, it runs in prod - How to build in your preferred environment with dbt Studio IDE or the dbt VS Code extension, with built-in linting, live feedback, and dialect-aware development workflows ##### Collaborate: - How to scale collaboration across teams with dbt Mesh, connecting domain-owned projects while keeping ownership, contracts, and governance clear - How to align analytics work around consistent, governed metrics powered by the dbt Semantic Layer ##### Operate: - How to schedule and orchestrate pipelines with built-in job scheduling, logging, and alerting - How to skip unnecessary model runs with dbt State, rebuilding only what has changed to reduce compute costs and accelerate delivery - How to manage and observe spend with Cost Insights ##### AI and agents: - How to ship models faster with dbt Wizard, an AI agent purpose-built for governed analytics engineering that understands your project context, lineage, tests, contracts, and metrics - How to build AI-ready workflows with the dbt MCP Server, giving AI agents and automated tools access to governed dbt project metadata and supported dbt actions **Who should attend:** - Data practitioners - Data engineers - Analytics engineers - Solution architects --- --- title: "dbt platform live demo - Benelux" description: "Watch a one-hour walkthrough of the dbt platform" url: "https://www.getdbt.com/resources/webinars/dbt-platform-live-demo-benelux" date: "2026-08-13" categories: ["Live Demo"] --- # dbt platform live demo - Benelux Watch a one-hour walkthrough of the dbt platform #### Built for agents and analytics you trust 🗓️ **Wednesday October 7th - 11am CET** Want to know how dbt helps teams build, test, and deploy AI-ready data pipelines faster? Join a live walkthrough of the dbt platform, tailored for data teams across the Benelux region. Watch a one-hour walkthrough of the dbt platform, including dbt Core v2.0, dbt Wizard, dbt State, and the full analytics engineering lifecycle in action. You'll see how dbt helps data teams move faster with more confidence — optimizing cloud data costs, accelerating development with purpose-built AI, and building the governed foundation behind trusted analytics and AI. **What you'll learn:** ##### Discover: - How to find, navigate, and understand resources in dbt Catalog, including project lineage, cross-project lineage, and column-level lineage - How to surface data health signals and downstream exposures across dashboards, applications, and data science pipelines to quickly assess quality, usage, and impact ##### Develop: - How to build faster with the dbt Fusion engine, using compiler-driven static analysis to catch errors before you run, so if it compiles in dev, it runs in prod - How to build in your preferred environment with dbt Studio IDE or the dbt VS Code extension, with built-in linting, live feedback, and dialect-aware development workflows ##### Collaborate: - How to scale collaboration across teams with dbt Mesh, connecting domain-owned projects while keeping ownership, contracts, and governance clear - How to align analytics work around consistent, governed metrics powered by the dbt Semantic Layer ##### Operate: - How to schedule and orchestrate pipelines with built-in job scheduling, logging, and alerting - How to skip unnecessary model runs with dbt State, rebuilding only what has changed to reduce compute costs and accelerate delivery - How to manage and observe spend with Cost Insights ##### AI and agents: - How to ship models faster with dbt Wizard, an AI agent purpose-built for governed analytics engineering that understands your project context, lineage, tests, contracts, and metrics - How to build AI-ready workflows with the dbt MCP Server, giving AI agents and automated tools access to governed dbt project metadata and supported dbt actions **Who should attend:** - Data practitioners - Data engineers - Analytics engineers - Solution architects - Ludwig Sewall --- --- title: "dbt platform demo - Nordics" description: "Watch a one-hour walkthrough of the dbt platform" url: "https://www.getdbt.com/resources/webinars/dbt-platform-live-demo-nordics" date: "2026-08-12" categories: ["Live Demo"] --- # dbt platform demo - Nordics Watch a one-hour walkthrough of the dbt platform #### Built for agents and analytics you trust 🗓️ **Wednesday October 7th - 11am CET** Want to know how dbt helps teams build, test, and deploy AI-ready data pipelines faster? Join a live walkthrough of the dbt platform, tailored for data teams across the Nordics. Watch a one-hour walkthrough of the dbt platform, including dbt Core v2.0, dbt Wizard, dbt State, and the full analytics engineering lifecycle in action. You'll see how dbt helps data teams move faster with more confidence — optimizing cloud data costs, accelerating development with purpose-built AI, and building the governed foundation behind trusted analytics and AI. **What you'll learn:** ##### Discover: - How to find, navigate, and understand resources in dbt Catalog, including project lineage, cross-project lineage, and column-level lineage - How to surface data health signals and downstream exposures across dashboards, applications, and data science pipelines to quickly assess quality, usage, and impact ##### Develop: - How to build faster with the dbt Fusion engine, using compiler-driven static analysis to catch errors before you run, so if it compiles in dev, it runs in prod - How to build in your preferred environment with dbt Studio IDE or the dbt VS Code extension, with built-in linting, live feedback, and dialect-aware development workflows ##### Collaborate: - How to scale collaboration across teams with dbt Mesh, connecting domain-owned projects while keeping ownership, contracts, and governance clear - How to align analytics work around consistent, governed metrics powered by the dbt Semantic Layer ##### Operate: - How to schedule and orchestrate pipelines with built-in job scheduling, logging, and alerting - How to skip unnecessary model runs with dbt State, rebuilding only what has changed to reduce compute costs and accelerate delivery - How to manage and observe spend with Cost Insights ##### AI and agents: - How to ship models faster with dbt Wizard, an AI agent purpose-built for governed analytics engineering that understands your project context, lineage, tests, contracts, and metrics - How to build AI-ready workflows with the dbt MCP Server, giving AI agents and automated tools access to governed dbt project metadata and supported dbt actions **Who should attend:** - Data practitioners - Data engineers - Analytics engineers - Solution architects - Ludwig Sewall --- --- title: "dbt platform live demo - UKI" description: "dbt platform demo - United Kingdom & Ireland" url: "https://www.getdbt.com/resources/webinars/dbt-platform-live-demo-uki" date: "2026-08-12" categories: ["Live Demo"] --- # dbt platform live demo - UKI dbt platform demo - United Kingdom & Ireland #### Built for agents and analytics you trust **🗓️ Tuesday September 29th - 12pm** Want to know how dbt helps teams build, test, and deploy AI-ready data pipelines faster? Join a live walkthrough of the dbt platform, tailored for data teams across the UK and Ireland. Watch a one-hour walkthrough of the dbt platform, including dbt Core v2.0, dbt Wizard, dbt State, and the full analytics engineering lifecycle in action. You'll see how dbt helps data teams move faster with more confidence — optimizing cloud data costs, accelerating development with purpose-built AI, and building the governed foundation behind trusted analytics and AI. **What you'll learn:** ##### Discover: - How to find, navigate, and understand resources in dbt Catalog, including project lineage, cross-project lineage, and column-level lineage - How to surface data health signals and downstream exposures across dashboards, applications, and data science pipelines to quickly assess quality, usage, and impact ##### Develop: - How to build faster with the dbt Fusion engine, using compiler-driven static analysis to catch errors before you run, so if it compiles in dev, it runs in prod - How to build in your preferred environment with dbt Studio IDE or the dbt VS Code extension, with built-in linting, live feedback, and dialect-aware development workflows ##### Collaborate: - How to scale collaboration across teams with dbt Mesh, connecting domain-owned projects while keeping ownership, contracts, and governance clear - How to align analytics work around consistent, governed metrics powered by the dbt Semantic Layer ##### Operate: - How to schedule and orchestrate pipelines with built-in job scheduling, logging, and alerting - How to skip unnecessary model runs with dbt State, rebuilding only what has changed to reduce compute costs and accelerate delivery - How to manage and observe spend with Cost Insights ##### AI and agents: - How to ship models faster with dbt Wizard, an AI agent purpose-built for governed analytics engineering that understands your project context, lineage, tests, contracts, and metrics - How to build AI-ready workflows with the dbt MCP Server, giving AI agents and automated tools access to governed dbt project metadata and supported dbt actions **Who should attend:** - Data practitioners - Data engineers - Analytics engineers - Solution architects - Rachel Ryan --- --- title: "From data chaos to agent-accessible: How to move up the AI data maturity curve" description: "Dive into the five-stage framework for AI data maturity, and what it takes to climb from one stage to the next." url: "https://www.getdbt.com/resources/webinars/from-data-chaos-to-agent-accessible-how-to-move-up-the-ai-data-maturity-curve" date: "2026-08-06" categories: ["On-Demand"] --- # From data chaos to agent-accessible: How to move up the AI data maturity curve Dive into the five-stage framework for AI data maturity, and what it takes to climb from one stage to the next. A CEO asks how many active customers the company has. Finance says one number, sales says another, ops says a third, all in the same thread. That's where most enterprises still are, and it's why AI pilots keep stalling before they reach production. This session introduces the Enterprise AI Data Maturity Model, a five-stage framework for assessing where your organization stands today and building a credible roadmap to AI that actually works. Russell Christopher, Senior Director of Product Strategy at dbt Labs and Petyo Pahunchev, Chief Product Officer at Infinite Lambda, will walk through what separates organizations generating real AI ROI from those still running experiments, digging into real examples. The stakes are real. Gartner puts the share of IT leaders worried about AI governance at 70%, and MIT research found 95% of enterprises are getting zero return from GenAI so far. The path to autonomous agentic systems runs directly through structured, governed data, not around it. Whether you're defending last year's AI budget or planning the next wave of investment, you'll leave knowing where your org sits today, what's blocking the next stage, and how to make the case for closing that gap. **What you'll walk away with** - The five stages of the Enterprise AI Data Maturity Model, and guidance on where your org sits today - Real before-and-after numbers from teams that moved up a stage - Three questions to bring back to your team on who owns closing the gap #### **Who should watch** - Data leaders and analytics executives shaping AI strategy - Data engineering and platform teams building agentic workloads **** #### **Meet the speakers** - Russell Christopher - Petyo Pahunchev --- --- title: "Building the interface for AI agents: MCP, the dbt Semantic Layer, and context engineering" description: "Learn how to make dbt-modeled data agent-ready using MCP and the dbt Semantic Layer." url: "https://www.getdbt.com/resources/webinars/building-the-interface-for-ai-agents-mcp-the-dbt-semantic-layer-and-context-engineering" date: "2026-08-04" categories: ["On-Demand"] --- # Building the interface for AI agents: MCP, the dbt Semantic Layer, and context engineering Learn how to make dbt-modeled data agent-ready using MCP and the dbt Semantic Layer. AI agents are only as good as the data they can reach. For analytics engineers, that raises a practical question: how do you expose governed, trusted data to an agent without losing the definitions and guardrails you've spent years building? In this session, Alex Noonan and Benoit Periguad, will walk through the best practices for making your data agent-ready using MCP, the dbt Semantic Layer, and how to think about context engineering. We'll cover the authoring side, how to structure and expose metrics so an agent gets consistent, correct answers and then move into live demos, including a walkthrough of Claude querying data through MCP. We'll close with a Q&A tackling the questions we hear most often from the community. You'll leave knowing how to turn your dbt project into a reliable foundation for agentic analytics. **Who should watch** - Data practitioners - Data engineers - Data analysts - Analytics engineers - Solution architects ##### **Meet the speakers** - Alex Noonan - Benoit Perigaud --- --- title: "dbt Core v1.12 Live: Release updates, the road to dbt Core v2.0" description: "Find out what shipped in v1.12, why it matters in practice, and how to plan your move to dbt Core v2.0" url: "https://www.getdbt.com/resources/webinars/dbt-core-v1-12-live" date: "2026-07-30" categories: ["On-Demand"] --- # dbt Core v1.12 Live: Release updates, the road to dbt Core v2.0 Find out what shipped in v1.12, why it matters in practice, and how to plan your move to dbt Core v2.0 The dbt framework continues to evolve through steady, thoughtful improvements, and dbt Core v1.12 is no exception. This release does two things at once. It delivers new features and enhancements for teams using dbt Core today, the kind that make dbt Core faster, more flexible, and easier to maintain over time. It also introduces a low-risk way to start getting ready for dbt Core v2.0 **In case you missed it, dbt Core v1.12 shipped with:** - A new opt-in Rust-based parser - The `on_error` model config, for more control when an upstream model fails - A dedicated `vars.yml` file for project variables - New `--sql` flag for `dbt run-operation` to execute ad hoc database statements - Expanded UDF support, including new JavaScript UDFs and broader Python UDF coverage - A new Semantic Layer YAML spec - Native private packages, a simplified Iceberg catalog spec, and clearer error messages You're invited to join members of the dbt Core engineering and developer experience teams for a live virtual event that's part deep dive, part release party, part roadmap preview. We'll open with a quick recap of dbt Core v2.0 - what it is, what it includes, and what it means for the dbt community. From there, we'll walk through what shipped in v1.12, why it matters in practice, and how to plan your move to v2.0. You'll see short demos of new features, hear about the dbt roadmap, and have plenty of time for live Q&A. Plus, we'll give away prizes to a few randomly selected live attendees! 🎉 **You will learn how to:** - **Better understand dbt v1, v2, and Fusion:** Get a clear, plain-language picture of how dbt Core 1.x, dbt Core v2.0, and Fusion relate to each other - **Understand what shipped in dbt Core v1.12:** Get a clear overview of the most meaningful updates without digging through release notes. - **Plan your move to v2.0:** Get resources to start planning your upgrade. - **Get answers from the dbt Core team:** Ask your questions live. **Who should attend:** - Analytics engineers and data engineers using dbt Core - Adapter maintainers and contributors - Teams planning to upgrade to dbt Core v2.0 - Anyone interested in the future direction of dbt Core #### Meet the dbt Core team speakers ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/9587816906afaa844c61137e0f39e079c2d937e6-1200x300.png) --- --- title: "dbt State in practice: what it is, how it works, and how to tune it" description: "A deep dive into how dbt State spots real changes, skips needless rebuilds, and what it costs to turn on." url: "https://www.getdbt.com/resources/webinars/dbt-state-in-practice" date: "2026-07-29" categories: ["On-Demand"] --- # dbt State in practice: what it is, how it works, and how to tune it A deep dive into how dbt State spots real changes, skips needless rebuilds, and what it costs to turn on. Ever watched a model rebuild just because someone added a comment? dbt State is how dbt tells the difference. In this session, we're getting into the details on how it actually works, with in-depth examples from the real scenarios. **What you'll learn:** - **What dbt State is**: a fast overview for anyone new to the concept. - **How it works**: the combination of checking a model’s content, the meaning of the rendered SQL and the modified times of upstream database objects lets dbt State rebuild what’s changed and skip what hasn’t. - **In-depth examples**: how dbt State handles volatile SQL (like `getdate()`) and Jinja macros (like `{{ invocation_id }}`); how to configure and customize freshness checks; how time travel makes it possible to clone models based on your local code; and which configs can change the meaning of a table — and how dbt State avoids rebuilding models just because you added a tag. - **Understanding the billing model:** how Daily Active Target Tables are calculated, optimization tips, and how to use the free trial. - **Installation & feedback**: how to enable dbt State across dbt versions and deployment strategies, and where to share feedback. This session’s demos will focus on locally hosted dbt Core v1 deployments, but the same principles will apply if you are using dbt Core v2.0, the dbt Fusion engine, or deploying through the dbt platform. **Who should tune in** - **Data engineers and analytics engineers** — If you're running dbt today and paying for warehouse compute, dbt State was built for you. Come see what you've been leaving on the table. - **dbt Core users** — This isn't platform-only. We'll walk through the local experience start to finish, including pip install. - **Technical leaders** — Warehouse costs are real. dbt State is one of the most direct levers you have to reduce them. **Meet the Speakers** - Joel Labes - Reuben McCreanor --- --- title: "dbt platform demo" description: "Watch a one-hour walkthrough of the dbt platform" url: "https://www.getdbt.com/resources/webinars/dbt-platform-live-demo" date: "2026-06-27" categories: ["On-Demand"] --- # dbt platform demo Watch a one-hour walkthrough of the dbt platform #### **Built for agents and analytics you trust** Want to know how dbt helps teams build, test, and deploy AI-ready data pipelines faster? Watch a one-hour walkthrough of the dbt platform, including dbt Core v2.0, dbt Wizard, dbt State, and the full analytics engineering lifecycle in action. You’ll see how dbt helps data teams move faster with more confidence — optimizing cloud data costs, accelerating development with purpose-built AI, and building the governed foundation behind trusted analytics and AI. **What you'll learn:** **Discover:** - How to find, navigate, and understand resources in dbt Catalog, including project lineage, cross-project lineage, and column-level lineage - How to surface data health signals and downstream exposures across dashboards, applications, and data science pipelines to quickly assess quality, usage, and impact **Develop:** - How to build faster with the dbt Fusion engine, using compiler-driven static analysis to catch errors before you run, so if it compiles in dev, it runs in prod - How to build in your preferred environment with dbt Studio IDE or the dbt VS Code extension, with built-in linting, live feedback, and dialect-aware development workflows **Collaborate:** - How to scale collaboration across teams with dbt Mesh, connecting domain-owned projects while keeping ownership, contracts, and governance clear - How to align analytics work around consistent, governed metrics powered by the dbt Semantic Layer **Operate:** - How to schedule and orchestrate pipelines with built-in job scheduling, logging, and alerting - How to skip unnecessary model runs with dbt State, rebuilding only what has changed to reduce compute costs and accelerate delivery - How to manage and observe spend with Cost Insights **AI and agents:** - How to ship models faster with dbt Wizard, an AI agent purpose-built for governed analytics engineering that understands your project context, lineage, tests, contracts, and metrics - How to build AI-ready workflows with the dbt MCP Server, giving AI agents and automated tools access to governed dbt project metadata and supported dbt actions **Who should attend:** - Data practitioners - Data engineers - Analytics engineers - Solution architects #### Meet the speakers: - Sara Gawlinski - Lee Bond-Kennedy --- --- title: "dbtプロダクトアップデート2026" description: "dbtプロダクトアップデート2026ウェビナー。dbt Core v2.0、Fusion、dbt State、dbt Wizard について、日本語でわかりやすく解説します" url: "https://www.getdbt.com/jp/resources/webinars/dbt-product-update-2026" date: "2026-06-19" categories: ["On-Demand"] --- # dbtプロダクトアップデート2026 dbtプロダクトアップデート2026ウェビナー。dbt Core v2.0、Fusion、dbt State、dbt Wizard について、日本語でわかりやすく解説します 2026年6月、Fivetran と dbt Labs の合併が発表され、両社は「信頼できるエージェントを支える、オープンで可搬性のあるデータ基盤」を共通のビジョンとして打ち出しました。あわせて dbt では、今後の開発・運用のあり方を大きく変える複数のアップデートが発表されています。 今回の無料ウェビナーでは、そうした最新発表の中でも特に注目度の高い dbt Core v2.0、Fusion、dbt State、dbt Wizard について、日本語でわかりやすく解説します。単なる機能紹介ではなく、「何が変わるのか」「現場にどんなインパクトがあるのか」「どのチームに特に効くのか」まで、実務目線でお伝えします。 アップデート内容： - v2.0 - dbt State - dbt Wizard **ウェビナー詳細** 日時：7月16日（木）14:00−15:00 形式：オンライン 参加費：無料 ※当日のご都合がつかない方も、ご登録いただいた方に、後日レコーディングをお送りします。ぜひお気軽にご登録ください。 **講演者** - 伊藤　俊廷 - 三角　勇貴 --- --- title: "Was gibt es Neues bei dbt + Fivetran" description: "Wir haben im Juni die Fusion von Fivetran + dbt Labs bekannt gegeben – und vier bahnbrechende Neuerungen vorgestellt." url: "https://www.getdbt.com/de/resources/webinars/dbt-neues-de" date: "2026-06-12" categories: ["On-Demand"] --- # Was gibt es Neues bei dbt + Fivetran Wir haben im Juni die Fusion von Fivetran + dbt Labs bekannt gegeben – und vier bahnbrechende Neuerungen vorgestellt. #### **Open Data Infrastructure, dbt State und dbt Wizard – live auf Deutsch** **On-Demand-Webinar – Aufzeichnung vom 9. Juli 2026** Im Juni 2026 haben Fivetran und dbt Labs ihren finalen Zusammenschluss bekanntgegeben. Im Vorfeld haben unsere Projektteams bereits an kritischen Innovationen gearbeitet, die auf die immerwährende Veränderung im Zeitalter von KI die richtigen Antworten für Data & Analytics Teams liefern. In diesem Live-Webinar wollen wir diese Antworten präsentieren - konkret geht es um: - Was bedeutet _Open Data Infrastructure (ODI) _und welche Mehrwerte hat mein Unternehmen davon? - Welche Tools bringen Data & Analytics Teams auf die nächste Produktivitäts- und Effizienz-Ebene? **Was wir zeigen?** - **Fivetran + dbt Labs**: Gemeinsam bauen wir die Open Data Infrastructure, mit dem Ziel, Kunden maximale Freiheitsgrade in ihrer Datenlandschaft zu ermöglichen. Wir erklären, was das für deine Architektur heute und morgen bedeutet. - **dbt State**: Mit dbt lösen Unternehmen schon heute ineffiziente veraltete Pipelines ab und erzielen deutliche Kosteneinsparungen. Aber es geht noch mehr! dbt State ist eine Metadaten-getriebene dbt Pipeline-Orchestrierung, die nur das ausführt, was sich tatsächlich geändert hat. Das Ziel: Compute-Einsparungen ohne Aufwand. - **dbt Wizard**: Der erste KI-Agent, der wirklich dein dbt-Projekt versteht. Kein generischer Coding-Assistent, sondern eine zweckmäßige Agent-Harness für Data & Analytics Teams, die bereits mit dbt setzen. - **dbt Core v2.0**: Auch die neue Firma setzt weiterhin auf die Stärke von Open Source Software. Vor 10 Jahren wurde der erste dbt-core code geschrieben und bis heute wurde dbt über 1 Milliarden Mal heruntergeladen. Mit v2.0, liefern wir ein gemeinsames Fundament für alle dbt Distributionen, egal ob self-hosted oder dbt platform. **In diesem Webinar nehmen wir alle Neuigkeiten gemeinsam unter die Lupe - Live-Q&A inklusive.** Du bist Analytics Engineer, Data Engineer, Data Product Manager oder leitest ein Team, dass dbt bereits einsetzen oder evaluieren, dann ist dieses Webinar genau das richtige für dich. **Ein Webinar präsentiert von:** - Stephan Durry - Peter Jehl - Marc an Voort - Markus Bergmaier --- --- title: "Ship faster, spend less, and trust your data — for AI and beyond" description: "In this webinar, we'll show you what changes when you bring that toolchain together under the dbt platform." url: "https://www.getdbt.com/resources/webinars/ship-faster-spend-less-and-trust-your-data-for-ai-and-beyond" date: "2026-06-10" categories: ["On-Demand"] --- # Ship faster, spend less, and trust your data — for AI and beyond In this webinar, we'll show you what changes when you bring that toolchain together under the dbt platform. You've built something real with dbt Core. But as your practice grows, the toolchain around it starts to become the bottleneck — orchestration owned by one person, CI/CD scripts that require constant upkeep, analysts waiting on a short list of engineers. And it all adds up in engineering hours, warehouse compute costs with no easy levers to pull, and data quality gaps that become a real problem as you start putting AI to work. In this webinar, we'll show you what changes when you bring that toolchain together under the dbt platform — and what it's worth in time, money, and AI readiness. You'll hear how one team reduced dbt-related compute costs by 45%, another cut time-to-insight from two weeks to 30 minutes, and another enabled 50 analysts to build independently within a month. **What you'll walk away with** - A concrete look at where engineering time and warehouse spend are quietly leaking — and how to get both back - How teams at your stage have reduced compute costs without disrupting what's already working - What "governed self-service" looks like in practice, so more people can build without more things breaking - How a governed Semantic Layer gives your AI agents and BI tools a single source of trusted context — so your AI investments actually pay off **Who should attend** Built for data engineers, analytics engineers, and data team leads who've gotten real mileage out of dbt Core and are ready to take their data practice — and their team's efficiency — to the next level. --- --- title: "Virtual dbt + Snowflake hands-on lab" description: "Raw data to AI agent in one session. Build a production-ready pipeline with dbt and Snowflake Cortex." url: "https://www.getdbt.com/resources/webinars/dbt-snowflake-virtual-hands-on-lab-build-a-production-ai-agent-grounded-in-trusted-data" date: "2026-05-28" categories: ["On-Demand"] --- # Virtual dbt + Snowflake hands-on lab Raw data to AI agent in one session. Build a production-ready pipeline with dbt and Snowflake Cortex. ### Build a production-ready AI agent grounded in trusted data In this virtual session you'll build a complete pipeline — from raw data through dbt transformations to a Snowflake Cortex agent that answers natural language questions and takes action. Not a demo. A production-style architecture you can take back to your team. By the end, you'll have: - A dbt project running in the dbt platform, with tested, documented models and Semantic definitions defined via the dbt Semantic Layer & Snowflake Semantic Views - A Cortex agent wired to that semantic layer, capable of answering questions and calling custom functions - A working mental model for how dbt and Snowflake fit together in an AI-first data stack AI agents are becoming the primary consumers of data. But agents are only as reliable as the data beneath them — and most pipelines weren't built with that in mind. dbt brings version control, testing, and documentation to your transformations. Snowflake Cortex activates that governed foundation for AI. Together, they let you build agents that don't hallucinate answers from a black box — they query data you've already tested and trust. **What to expect** **Part 1 — Transform data with dbt** You'll build a production dbt pipeline inside the dbt platform, defining semantics in two ways: using the dbt Semantic Layer and via the `snowflake_semantic_view` package to define a Snowflake Semantic View directly from your dbt project. You'll commit your work via Git and run a production job end-to-end. **Part 2 — Build a Cortex agent** You'll create a Cortex agent from scratch — connecting it to your Semantic View, adding custom SQL functions as tools, and testing it in Snowflake. You'll watch it answer natural language queries, filter data, and route structured alerts without leaving the platform. **Who should join** - Analytics engineers building with or evaluating dbt - Data engineers working in Snowflake - Data analysts ready to level up from SQL to semantic layers - Anyone thinking about how to connect their existing dbt work to AI tooling **What you'll walk away with** - Hands-on experience with the dbt platform, the dbt Semantic Layer, Snowflake Semantic Views, and Snowflake Cortex - A reusable architecture pattern — same approach works for customer data, financial metrics, operational reporting - The lab repo to reference and adapt for your own stack - Luis Leon --- --- title: "Fivetran + dbt Labs: The future of dbt Core v2.0, what we're building together, and live Q&A" description: "Learn about dbt Core v2.0, dbt State, and dbt Wizard, AI Connector Builder, Agents Schema, and dbt Charts." url: "https://www.getdbt.com/resources/webinars/fivetran-dbt-labs-the-merger-what-s-shipping-in-dbt-and-live-q-and-a" date: "2026-05-27" categories: ["On-Demand"] --- # Fivetran + dbt Labs: The future of dbt Core v2.0, what we're building together, and live Q&A Learn about dbt Core v2.0, dbt State, and dbt Wizard, AI Connector Builder, Agents Schema, and dbt Charts. Fivetran and dbt Labs are officially joining forces to deliver to deliver the open data infrastructure organizations need to build and trust AI agents. In this on demand virtual event, Tristan Handy (Co-founder and President, Fivetran + dbt Labs) and Taylor Brown (Co-founder and COO, Fivetran + dbt Labs) walk through what the merger means for you: why we're bringing these companies together, the latest product innovations from Fivetran and dbt Labs, what's available today, and where we're headed next. **What you’ll walk away with** - A clear picture of why Fivetran and dbt Labs joined forces and what the combined company is building - The story behind dbt Core v2.0 — what changed, what didn't, and why Apache 2.0 - A first look at all the new features that Fivetran + dbt Labs launched on June 1: dbt State, dbt Wizard, AI Connector Builder, Agents Schema, and dbt Charts - Your questions answered live **Who should tune in** **Data engineers and analytics engineers** — Tristan will walk through dbt Core v2.0 and share a first look into two new products built for practitioners (dbt State and dbt Wizard). If you're building in dbt today, this one's for you. **Technical leaders** — Get the strategic context behind the merger, the open data infrastructure vision, and what the dbt + Fivetran roadmap means for how your team works. **AI/ML practitioners and platform engineers** — the merger exists because of the problems you're trying to solve. Tristan and Taylor will walk through why open data infrastructure is the missing piece for agents you can actually trust. **Anyone who's been wondering what the merger actually means** — This is the most direct answer you'll get. Tristan and Taylor are taking live questions. Come with yours. #### Meet the speakers - Taylor Brown - Tristan Handy ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/603b458c15c798a45b7e7373b5978b61c082ebfb-4675x1000.png) *dbt State Webinar* ## Go deeper on dbt State. Build what's changed, skip what hasn't. Join us July 15th for demos of the platform and local experience, a real customer story, and Q&A. Learn how to achieve real compute savings and hours back for your team to focus on what actually matters. [Save your seat](https://www.getdbt.com/resources/webinars/dbt-state-build-what-s-changed-skip-what-hasn-t/?utm_medium=internal&utm_source=www&utm_campaign=q2-2027_dbt-state-deep-dive-product_aw&utm_content=themed-webinar____&utm_term=all_all__) *** *dbt Wizard Webinar* ## Dive in with dbt Wizard. Join us July 22nd to see it in action across the CLI and dbt platform — building models, debugging failures, refactoring, and more. Plus hear how real analytics teams are using it today and get your questions answered live. [Save your seat](https://www.getdbt.com/resources/webinars/dbt-wizard-an-agent-purpose-built-for-analytics-engineering/?utm_medium=internal&utm_source=www&utm_campaign=q2-2027_dbt-wizard-deep-dive_aw&utm_content=themed-webinar____&utm_term=all_all__) --- --- title: "dbt Semantic Layer workshop: From models to metrics" description: "Learn how to use the dbt Semantic Layer to define, query, and deliver metrics your whole team can trust." url: "https://www.getdbt.com/resources/webinars/from-models-to-metrics-dbt-semantic-layer-workshop" date: "2026-05-27" categories: ["On-Demand"] --- # dbt Semantic Layer workshop: From models to metrics Learn how to use the dbt Semantic Layer to define, query, and deliver metrics your whole team can trust. #### 🌐 Global friendly sessions on June 17 & 18 Ready to bring consistency to your metrics with the dbt Semantic Layer? Join us for this virtual workshop. You'll come away with a clear understanding of what it is, what it's for, and when it makes sense to use it in your projects — including how keeping metrics in code and version-controlled changes the way your team works with them. To dive in, you should already be familiar with SQL and dbt. If you've had experience with dbt Core, the dbt platform, or tackled dbt Fundamentals, you're all set. In this session, you'll work directly with experienced dbt practitioners through demos, active chat, and Q&A. Don't stress if you need more time to keep up — the entire workshop will be recorded so you can revisit the material and complete your project at your own pace. **What you'll learn:** - **Hands-on configuration:** learn to set up entities, dimensions, and measures within your semantic models, and how to configure and use metrics effectively. - **Querying the Semantic Layer:** discover how to query the dbt Semantic Layer so your teams and tools are always pulling from the same governed metric definitions. - **A touch of dbt Wizard:** see how dbt Wizard accelerates semantic model creation, so you spend less time on setup and more time on what your metrics actually need to do. **Who should join** This workshop is for analytics engineers, data engineers, and data analysts who are ready to move beyond ad hoc metric definitions and build a consistent, queryable semantic layer. If you've been curious about how to make metrics reusable across tools — or your team keeps asking "which number is right?" — this session is for you. **What you'll walk away wtih** - A working semantic model you built yourself, ready to take back to your project - Confidence in how to structure entities, dimensions, and measures and why the distinctions matter - Practical patterns for keeping metrics consistent and trustworthy across your organization - Jenna Bushspies --- --- title: "dbt State: Build what's changed, skip what hasn't" description: "Achieve fewer unnecessary model runs, real compute savings, and hours back for your team to focus on work that actually matters." url: "https://www.getdbt.com/resources/webinars/dbt-state-build-what-s-changed-skip-what-hasn-t" date: "2026-05-27" categories: ["On-Demand"] --- # dbt State: Build what's changed, skip what hasn't Achieve fewer unnecessary model runs, real compute savings, and hours back for your team to focus on work that actually matters. You've been rebuilding models you didn't need to. Every run. Every time. It's slowing down your pipelines, burning warehouse compute, and keeping your team focused on the wrong problems. dbt State changes that. In this on demand virtual event, we will show you the value of dbt State: fewer unnecessary model runs, real compute savings, and hours back for your team to focus on work that actually matters. See it running in the dbt platform and locally as a dbt Core user, hear a customer story from [Fanatics Betting and Gaming](https://www.fanaticsinc.com/fanatics-betting-gaming) with real compute savings, and get your questions answered by the people who built it. You'll leave knowing whether dbt State is right for your stack, and exactly how to get started. **What you’ll walk away with** - A clear understanding of what dbt State is, how it works, and who can access it - A demo of dbt State in the platform — turning it on, watching models get skipped, seeing the execution time graph - A walkthrough of the local/dbt Core experience so you can get started wherever you work - A real customer story showing actual compute savings - Answers to the questions practitioners always ask — lag tolerance, configuration, and what it looks like in practice **Who should tune in** **Data engineers and analytics engineers** — If you're running dbt today and paying for warehouse compute, dbt State was built for you. Come see what you've been leaving on the table. **dbt Core users** — This isn't platform-only. We'll walk through the local experience start to finish, including pip install. **Technical leaders** — Warehouse costs are real. dbt State is one of the most direct levers you have to reduce them. See the numbers. #### **Meet the speakers** - Jimmy Hooker - Reuben McCreanor - Alvin Chai ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/603b458c15c798a45b7e7373b5978b61c082ebfb-4675x1000.png) --- --- title: "dbt on Microsoft Azure Multi-tenant AU" description: "Multi-tenant dbt on Azure, now in Australia" url: "https://www.getdbt.com/resources/webinars/dbt-on-microsoft-azure-multi-tenant-au" date: "2026-05-18" categories: ["On-Demand"] --- # dbt on Microsoft Azure Multi-tenant AU Multi-tenant dbt on Azure, now in Australia Microsoft Azure Multi-tenant now available in Australia! Join us to learn more about what this means for your Azure powered data stack and secure connectivity. We'll also see a demo of how to set up your first dbt Project powered by dbt Fusion, and share links for how to get started yourself. We will also showcase how dbt State helps data teams only run models that actually needs to be executed. Why is dbt State so important? dbt State automatically determines which models to build by detecting changes in code or data which can help you optimize your compute costs job runtime. **In the demo:** - Introduction to the dbt platform - How to use dbt as part of your testing and data quality checks - Using dbt Copilot to write, edit, and explain code - Test, document, and deploy your data transformations - Overview of how dbt Fusion improves Developer Experience & dbt State optimizes runtime **When** - Tuesday, 23 June 2026 - 1:30PM - 2:15PM (AEST) | 3:30PM - 4:15PM (NZST) **Who should attend this workshop** If you’re able to read and write basic SQL & you want to learn more about how dbt can help you deliver reliable, trusted data whilst providing cost savings, you should attend this workshop. - Data practitioners - Data engineers - Data analysts - Analytics engineers - Solution architects **Presenter** - Lee Bond-Kennedy --- --- title: "Get your data AI ready with dbt and Snowflake" description: "Data without context isn't AI ready. Learn how dbt and Snowflake give AI the structure it needs to work." url: "https://www.getdbt.com/resources/webinars/get-your-data-ai-ready-with-dbt-and-snowflake" date: "2026-05-08" categories: ["On-Demand"] --- # Get your data AI ready with dbt and Snowflake Data without context isn't AI ready. Learn how dbt and Snowflake give AI the structure it needs to work. #### 🌐 Global friendly sessions on May 27 & 28 AI is only as good as the data powering it. Most organizations have plenty of data, but data without context, structure, and shared meaning isn't something AI can work with reliably. In this session, you'll learn how dbt and Snowflake work together to close that gap by embedding rich business context into your data. When you build semantic models in dbt, you're doing more than transforming tables. You're creating documented business logic, shared definitions, and data quality signals that reflect how your organization actually operates. That's the context AI needs to be useful. This semantic layer feeds directly into agentic and AI-driven applications, including Snowflake Intelligence. The result: more accurate responses, fewer hallucinations, and natural language querying your whole organization can trust. If you're responsible for data transformation, preparing your platform for AI adoption, or scaling analytics, this session will show you what it takes to make your data AI-ready and how dbt and Snowflake get you there. **What you'll learn** - Why data quality and semantic foundations are essential for AI readiness and successful AI initiatives - How to build unified data pipelines that harmonize structured and unstructured data analysis using dbt and Snowflake Cortex - Practical techniques for incorporating Cortex AI functions (sentiment analysis, summarization, extraction) directly within dbt models - How to create and deploy Snowflake Semantic Views from dbt semantic models for consistent, governed AI consumption - How to expose dbt semantics to AI applications using the dbt MCP Server for natural language analytics - Real-world patterns for enabling Cortex Analyst with rich semantic context from your dbt projects - Best practices for scaling AI adoption while maintaining data trust, quality, and governance **Who should join** This session is for data and technology leaders responsible for building and scaling enterprise data and AI platforms, including: - Chief data officers and digital transformation leaders - Data, analytics, and technology leaders - AI, ML, and data science leaders focused on production-ready AI - Enterprise data architects and platform leaders - Data engineering and platform teams building on Snowflake Whether you're exploring AI use cases, operationalizing Snowflake Intelligence, or standardizing semantics with dbt, this session will help you deliver more accurate, trusted, and scalable AI outcomes across the enterprise. **Speakers** - Luis Leon - Michael Taylor --- --- title: "Community feedback session: Iceberg catalogs, revisited" description: "Join a casual session with engineers to share feedback, explore Iceberg improvements, and influence what’s next." url: "https://www.getdbt.com/resources/webinars/community-feedback-session-iceberg-catalogs-revisited" date: "2026-04-17" categories: ["On-Demand"] --- # Community feedback session: Iceberg catalogs, revisited Join a casual session with engineers to share feedback, explore Iceberg improvements, and influence what’s next. #### You're invited - April 28, 12:00p ET Want to help shape the future of dbt? Join dbt Labs engineers and product folks for an informal community feedback session where we'll share the problem space, where we're headed, and open it up for whatever comes up. It's casual, fun, and impactful. Come with questions, opinions, and skepticism. These sessions are one of the best ways to give back to the dbt community and have a real say in what gets built — many dbt features have been shaped directly by conversations like this one. Past sessions have covered microbatch incremental models, the dbt Core 1.11 release, and other major features. **Prerequisites:** Attendees should be familiar with Iceberg at a high level, but not experts. Give the full proposal a read ahead of time: [dbt-core#12723: Iceberg catalogs, revisited](https://github.com/dbt-labs/dbt-core/discussions/12723). #### What we're proposing A lot of work has gone into Iceberg support in dbt — nearly every adapter has it built in, and the `catalogs.yml` construct abstracts away much of the nuance of working with different catalogs across platforms. But we think it can be easier to use, more future-proof, and better suited to the cross-platform workflows that Iceberg makes possible. We have a proposal and a demo to share — and we need your feedback. #### Details **Session details** - April 28, 2026 - 12:00p - 1:00p ET A link to join will be sent via confirmation email after registration. Can’t make the live event? We will send out a recording within 24 hours after the event! **Meet your speakers** - Anders Swanson - Mila Page - Jeremy Cohen - Anna Lee --- --- title: "dbt Labs + Microsoft Fabric in action: A step-by-step analytics engineering demo" description: "See dbt + Microsoft Fabric in action—build, test, and deploy a full analytics workflow live." url: "https://www.getdbt.com/resources/webinars/dbt-labs-microsoft-fabric-in-action-a-step-by-step-analytics-engineering-demo" date: "2026-04-17" categories: ["On-Demand"] --- # dbt Labs + Microsoft Fabric in action: A step-by-step analytics engineering demo See dbt + Microsoft Fabric in action—build, test, and deploy a full analytics workflow live. Modern analytics teams need a stack that's both powerful and flexible — and dbt platform paired with Microsoft Fabric delivers exactly that. In this hands-on session, we'll walk you through a complete, end-to-end analytics engineering workflow from raw data to trusted, governed models, with every step happening live in real time. You'll watch us connect dbt platform to Microsoft Fabric, structure a project with sources, staging models, and marts, write and test transformations, and deploy to production — so you can follow along and replicate it yourself immediately after. Whether you're exploring dbt platform and Fabric for the first time or looking for the confidence to bring this stack into a real project, this session gives you a concrete, repeatable path forward. **What you'll see demoed** - Setting up a dbt project connected to Microsoft Fabric - Writing modular SQL transformations with dbt models - Adding tests and documentation to your pipeline - Running and deploying your project end to end No prior dbt experience required — just bring your curiosity and a willingness to get hands-on. #### Speakers **Meet your speakers** - Nina Anderson - Pradeep Srikakolapu --- --- title: "The future is open: Building a flexible, AI-ready data stack without lock-in" description: "Data teams can’t rebuild everything at once. Learn how open, modular stacks create flexibility—and power reliable, AI-ready data." url: "https://www.getdbt.com/resources/webinars/the-future-is-open-building-a-flexible-ai-ready-data-stack-without-lock-in" date: "2026-04-17" categories: ["On-Demand"] --- # The future is open: Building a flexible, AI-ready data stack without lock-in Data teams can’t rebuild everything at once. Learn how open, modular stacks create flexibility—and power reliable, AI-ready data. Most data teams don't have time to rebuild their entire data stack at once. They solve one problem at a time, and the tools they choose either lock them in or leave them free to grow. Leaders from Fivetran, dbt Labs, and Omni will break down what it actually means to build an open, modular data stack, and why decoupled architecture creates real flexibility at every layer from ingestion to insight. They’ll also cover how the context you have across tools can be brought together to build a data infrastructure for reliable AI. Then, Darren Peters from ACV Auctions will join to share how they approached their data stack to build AI-powered experiences for stakeholders. He’ll cover the challenges they faced with legacy tools and what they’re building for stakeholders — including how AI-assisted workflows are changing the way the data team and product managers collaborate. You’ll learn: - Ways to create flexibility with a decoupled architecture - How customers use Fivetran, dbt, and Omni to build a foundation for reliable AI - How ACV Auctions moved from legacy reporting to more modern, faster workflows #### Details **Who should attend** - Chief Data Officer (CDO) / VP, Data & Analytics - Chief Technology Officer (CTO) - Head of Data Platform / Architecture - Head of Analytics Engineering - Head of AI / ML Engineering #### Speakers **Meet your speakers** - Russell Christopher - Chris Merrick - Amy Peterson - Darren Peters --- --- title: "The next era of data: Build an open infrastructure for scale and AI" description: "Move beyond fragmented tools. Build an open data infrastructure for scalable, governed, AI-ready data." url: "https://www.getdbt.com/resources/webinars/the-next-era-of-data-build-an-open-infrastructure-for-scale-and-ai" date: "2026-04-16" categories: ["On-Demand"] --- # The next era of data: Build an open infrastructure for scale and AI Move beyond fragmented tools. Build an open data infrastructure for scalable, governed, AI-ready data. The modern data stack is evolving. As organizations scale, many are finding that even with best-in-class warehouse solutions, fragmented ingestion and transformation tooling can create challenges around reliability, governance, and flexibility. Ultimately, this means that data is not as valuable as it can and should be. In this session, we’ll explore the next phase of modern data architecture: open data infrastructure (ODI). Built on open standards, ODI enables teams to move beyond monolithic, vendor-locked solutions, and toward a modular, interoperable approach, where the right tools can be used for the right workloads across clouds and platforms. We’ll break down what this shift looks like in practice, why leading organizations are moving in this direction, and how dbt Labs helps power a flexible, future-ready architecture. Join us to learn how to modernize your data stack to meet business demands of today, and how to start building toward an open, scalable foundation for the next generation of data and AI. #### Details **Who should attend** - Data and technology executives - CIO - CDO - CTO - CDAIO - Heads of Data - Data architecture and analytics engineering leaders - Data architects - Analytics engineering leaders #### Meet your speakers **Speakers** - Kyle Dempsey - David Gelman, Ph.D. --- --- title: "How Nasdaq productized a governed intelligence layer with dbt + Databricks for financial market infrastructure" description: "How Nasdaq, dbt, and Databricks help FMIs scale governed data and enable AI-ready, trusted analytics." url: "https://www.getdbt.com/resources/webinars/how-nasdaq-productized-a-governed-intelligence-layer-with-dbt-databricks-for-financial-market" date: "2026-04-10" categories: ["On-Demand"] --- # How Nasdaq productized a governed intelligence layer with dbt + Databricks for financial market infrastructure How Nasdaq, dbt, and Databricks help FMIs scale governed data and enable AI-ready, trusted analytics. Mid‑to‑small Financial Market Infrastructures (FMIs) often have plenty of data—but not the resources to make it usable, consistent, and trustworthy without years of engineering investment. Nasdaq’s Intelligence offering changes that by delivering a blueprint as a service: a governed foundation that can be deployed repeatedly across markets and customer environments. In this panel, Nasdaq, dbt, and Databricks discuss how they built and scaled this approach across trading, post‑trade, and CSD workflows, while keeping definitions and contracts consistent as upstream systems evolve. We’ll share how Databricks provides the compute and infrastructure governance layer, and how dbt operationalizes standardized, auditable models through contracts, lineage, and reusable project patterns (dbt Mesh). Finally, we’ll explore why a governed semantic layer is the bridge to AI in financial services—enabling safe, defensible analytics and agentic experiences without risking “confident but wrong” outputs. #### Details **Who should attend** - Chief Data Officer (CDO) - Chief Technology Officer (CTO) - VP / Head of Data & Analytics - VP / Head of Data Platforms - VP / Head of Data Architecture - Head of AI / ML Engineering - Director of Data & Analytics - Director of Data Platforms - Director of Data Architecture #### Meet your speakers **Speakers** - Jamie Nemeroff - Andrea DeSosa - Michael Weiss #### Partners --- --- title: "2026 State of Analytics Engineering" description: "Join three top minds in data and analytics as they break down the biggest shifts shaping analytics engineering in 2026." url: "https://www.getdbt.com/resources/webinars/2026-state-of-analytics-engineering-virtual-event" date: "2026-03-31" categories: ["On-Demand"] --- # 2026 State of Analytics Engineering Join three top minds in data and analytics as they break down the biggest shifts shaping analytics engineering in 2026. Analytics teams are shipping faster than ever, with AI now embedded in how code gets written, tested, and deployed. At the same time, trust, governance, and cost control are under more pressure than ever. The results are in! The [2026 State of Analytics Engineering report is now live](https://www.getdbt.com/resources/state-of-analytics-engineering-2026) and it shows a clear shift: AI is accelerating output faster than foundations can keep up. So how are teams actually balancing speed with trust without breaking things? Watch a dynamic discussion with three top minds in data and analytics: **Katie Bauer (Hex), Jay Sobel (Ramp), and Jason Ganz (dbt Labs)** as they break down the biggest shifts shaping analytics engineering in 2026 and what they mean for your data work. **In this on-demand virtual event, you’ll get a clear, unfiltered view into how experienced teams are navigating:** - The shift from AI experimentation to everyday workflows - Where trust is breaking down—and how teams are addressing it - The tension between moving faster and maintaining control - Increasing infrastructure costs and the pressure to operate efficiently **What you’ll walk away with** - A grounded perspective on how analytics engineering is evolving right now - Real examples of how teams are using AI in production—not just experimenting - Practical ways to improve trust, governance, and ownership as you scale - Insight into where teams are investing (and where they’re pulling back) - A clearer sense of what to prioritize next for your team - Live Q&A: Bring your questions and join the conversation. We’re all ears!👂 #### Meet the speakers - Katie Bauer - Jay Sobel - Jason Ganz #### Host partner --- --- title: "Ship smarter agents: Building for production with dbt Agent Skills" description: "Learn how dbt Agent Skills give agents trusted access to your data layer and how Factory.ai moved from prototype to production." url: "https://www.getdbt.com/resources/webinars/ship-smarter-agents-building-for-production-with-dbt-agent-skills" date: "2026-03-27" categories: ["On-Demand"] --- # Ship smarter agents: Building for production with dbt Agent Skills Learn how dbt Agent Skills give agents trusted access to your data layer and how Factory.ai moved from prototype to production. AI agents are moving from demos to production. The teams winning are the ones who've given their agents real capabilities, not just chat interfaces. dbt Agent Skills are how you get there: native and custom skills that let agents reason about, navigate, and act on your data layer. In this session, our Head of Developer Experience will walk through the Agent Skills that ship with dbt today and how teams can extend them with custom skills built for your specific workflows. You’ll also hear from [Factory](http://Factory.ai), who will share how they built their software development agent, Droid, using dbt and custom skills. They’ll cover what it takes to move from prototype to production when agents need to work with data they can trust. You'll walk away understanding why skills are the right foundation for production agents, what you can build with dbt agent skills today, how to extend the ecosystem with your own custom skills and share them with the community, and how to evaluate whether your agent architecture is built to last. **Who should watch the session** - Data, analytics, or AI/ML engineers evaluating or building agentic workflows - Teams prototyping agents and looking to move to production - Engineers giving agents structured access to their data layer - Engineering leaders evaluating dbt's role in the agentic stack - Developers looking to build and contribute custom dbt skills #### Meet the speakers - Jason Ganz - Nikhil Harithas #### Host partner --- --- title: "Fast-Track Your Lakehouse: Accelerating Use Case Onboarding with dbt and Databricks" description: "Stop rewriting transformations. Shift existing dbt projects to Databricks and speed up use case delivery." url: "https://www.getdbt.com/resources/webinars/fast-track-your-lakehouse-accelerating-use-case-onboarding-with-dbt-and-databricks" date: "2026-03-20" categories: ["On-Demand"] --- # Fast-Track Your Lakehouse: Accelerating Use Case Onboarding with dbt and Databricks Stop rewriting transformations. Shift existing dbt projects to Databricks and speed up use case delivery. Move from ingestion to insights faster by running transformations directly on the Lakehouse—without rewriting existing transformation logic. - **No rewrite required:** Repoint existing dbt Core projects to Databricks - **Faster time to value:** Reduce the post-ingestion bottleneck - **End-to-end flow:** See how LakeFlow + embedded dbt can streamline pipelines **The problem** Getting raw data hydrated into the Databricks Lakehouse is a critical first step. But for many teams, the real bottleneck appears immediately after ingestion: - Migrating existing business logic from legacy warehouses - Onboarding new use cases quickly enough to maintain momentum - Avoiding long, risky transformation rewrites When there are thousands of lines of transformation code, starting over is not realistic. **The solution: Don’t rewrite. Repoint** If your team already relies on dbt Core to manage transformation logic in a traditional warehouse, the fastest path to Lakehouse value is to **upgrade to the managed dbt platform and repoint existing dbt projects to Databricks**. This approach lets teams keep proven transformation logic while shifting execution to Databricks compute - so use cases can be delivered faster with less refactoring and lower risk. **What you’ll learn** In this session, we’ll show how dbt is embedded into the Databricks ecosystem to support a smooth, secure migration experience. We’ll also explore how **Databricks LakeFlow** complements dbt by unifying ingestion and orchestration. Together, LakeFlow + dbt help teams build a more frictionless, end-to-end pipeline - from data landing to governed transformations. - **Accelerated onboarding:** How to shorten the time from raw data landing in the Lakehouse to delivering business value - **The power of repointing:** How to migrate existing dbt Core investments to the managed dbt platform on Databricks with minimal disruption - **LakeFlow synergy:** How LakeFlow + embedded dbt can streamline ingestion, orchestration, and transformation workflows - **Future-proofing:** How standardising transformations on the Lakehouse can improve governance and compute efficiency **Who should attend** - Data engineering and analytics engineering leaders - Platform and data architecture teams modernising from legacy warehouses - dbt Core users evaluating a path to the managed dbt platform on Databricks - Hicham Babahmed - Thor List --- --- title: "Rewrite the rules for data and AI: USAA’s path towards a more perfect pipeline" description: "Rewrite the rules for data and AI: dbt’s platform direction, plus USAA’s CI/CD and testing journey to restore trust." url: "https://www.getdbt.com/resources/webinars/rewrite-the-rules-for-data-and-ai-in-texas-fusion-ai-and-usaa-s-path-towards-a-more-perfect" date: "2026-03-16" categories: ["On-Demand"] --- # Rewrite the rules for data and AI: USAA’s path towards a more perfect pipeline Rewrite the rules for data and AI: dbt’s platform direction, plus USAA’s CI/CD and testing journey to restore trust. Join dbt Labs and USAA for a practical session on building an enterprise data foundation that's faster, more reliable, and ready for AI. First we’ll cover how dbt is building toward an open, interoperable data foundation and how the dbt Fusion engine is raising the bar on developer experience, performance, and efficiency. Then USAA will share what it took to move from a “Wild West” production environment to a system of continuous improvement. Expect a detailed look at environment strategy, testing, and CI/CD patterns that reduce errors, shorten feedback loops, and build confidence in every change. But that's only half the story. USAA will also walk through how they made AI a real contributor to their engineering processes — with a full Infrastructure as Code migration that brought structure and governance, then building out MCP server architecture that lets AI agents interact natively with dbt development workflows and data warehouses. You'll leave with concrete ideas to ship faster, reduce firefighting, and put AI to work in your dbt environment. **What you’ll gain from attending** - See how dbt teams are thinking about what’s next, including the Fusion roadmap and an open, interoperable data foundation - Learn practical patterns from USAA on building trust in pipelines through environments, testing, and CI/CD - Discover how to make AI genuinely useful in a production-ready dbt environment - Walk away with ideas you can apply to reduce errors, speed up delivery, and spend less time firefighting **Who should attend** - Data practitioners & dbt users - Analytics Engineers - Data Engineers and Data Platform Engineers - Analytics/Data Engineering Managers - Data team leads and platform owners **Session details** May 14, 1:00 PM CT Can't attend live? No worries! Register now, and we'll send you the recording and follow-up resources after the session wraps. #### **Meet your Speakers:** - Michael Sturm - Aaiden Witten - Russell Christopher #### Featured Customer: --- --- title: "Building dbt models faster with Google AI" description: "See how Gemini AI and the dbt MCP server automate model building, testing, and debugging for faster dbt workflows on BigQuery." url: "https://www.getdbt.com/resources/webinars/building-dbt-models-faster-with-google-ai" date: "2026-03-11" categories: ["On-Demand"] --- # Building dbt models faster with Google AI See how Gemini AI and the dbt MCP server automate model building, testing, and debugging for faster dbt workflows on BigQuery. AI is only as useful as the context it has. For data engineering, that context lives in dbt. Your models, lineage, tests, business logic, and documentation all live in one structured, queryable layer. It's the missing grounding layer between Google AI and your data platform. In this session, you'll see what happens when you connect them. We'll show how Google's Gemini CLI and Antigravity IDE plug into your dbt project through dbt's MCP server, giving AI agents real visibility into how your data is built, tested, and documented. Instead of a generic coding assistant, you get one that understands your pipeline and can act on it. We'll demo this live against real BigQuery data, showing how dbt agent skills let you delegate real work, including scaffolding models, writing data quality tests, diagnosing failures, and running lineage impact analysis, all without leaving your IDE. **You'll leave knowing how to** - Use dbt as a structured context layer to make Google AI dramatically more useful - Connect Gemini to your dbt project via MCP in your own environment - Delegate complex development tasks to AI using dbt agent skills - Move faster on BigQuery-native dbt patterns without starting from scratch If you’re exploring the future of AI-native data engineering, this session will give you a practical look at what’s possible today. - Stephen Robb - Jobin George #### **Host Partner** --- --- title: "Modeling ERP data in dbt: Best practices for reliable reporting" description: "Learn how to use dbt to transform raw ERP extracts into governed, tested, analytics-ready models" url: "https://www.getdbt.com/resources/webinars/modeling-erp-data-in-dbt-best-practices-for-reliable-reporting" date: "2026-03-03" categories: ["On-Demand"] --- # Modeling ERP data in dbt: Best practices for reliable reporting Learn how to use dbt to transform raw ERP extracts into governed, tested, analytics-ready models Your ERP is your most trusted system of record. It's also the hardest place to get a straight answer. SAP, Oracle ERP Cloud, and Dynamics 365 excel at what they were designed for — recording transactions and keeping operations running. Analytics is a different problem. Cross-functional reporting, historical reconstruction, and executive-ready insights require a layer that these systems were never meant to provide. The gap shows up fast: - Finance teams burning hours on manual reconciliations - Business logic buried in spreadsheets and legacy scripts that no one fully understands - Institutional knowledge disappears when key team members leave - M&A creates fractured reporting across multiple systems - Out-of-the-box ERP reports fall short of what leadership actually needs **You have the data. You're missing the foundation.** In this on-demand virtual event, data experts from [phData ](https://www.phdata.io/dbt/)and dbt Labs will show you how to use dbt to transform raw ERP extracts into governed, tested, analytics-ready models — with SQL, Jinja, and version control at the center of it all. During the session, we’ll demo how dbt models ERP data across real-world scenarios, including legacy migrations, ERP extracts, and multi-ERP environments. You’ll also receive access to a sample dbt project built around ERP modeling so you can explore the patterns yourself after the event. If you're done patching together fragile pipelines and inconsistent metrics, this session delivers a practical blueprint for centralizing business logic in dbt, eliminating shadow transformations, and building reporting that your leadership can stand behind. **What you will learn (with live demos)** **Why ERP reporting breaks down:** - How ERP systems are optimized for transaction integrity, not cross-functional analytics - Where hidden business logic, spreadsheets, and “shadow ETL” quietly accumulate over time - Why reconciliation issues and inconsistent metrics are often symptoms of unmanaged transformation logic - What makes “as-of-date” and historical reporting especially fragile without the right modeling approach **How Analytical Engineering bridges the gap:** - How to use dbt as the governed transformation layer between ERP extracts and reporting - How to centralize business logic in modular, version-controlled SQL models - How to formalize institutional knowledge with documentation, tests, and seeds - How snapshots enable reliable point-in-time analysis for finance and operations **How to apply these patterns in real ERP environments Through live demos, you’ll see how to:** - Model legacy ERP logic during system upgrades or migrations - Standardize and reuse transformation logic across company codes and extract modules - Support multi-ERP environments (including M&A scenarios) while building a trusted SSOT - Replace one-off scripts and manual reconciliation processes with repeatable, auditable pipelines **What you’ll walk away with** - A clear framework for reducing ERP technical debt - A strategy for codifying institutional knowledge into tested, documented dbt models - A practical approach to “as-of-date” analytics - A roadmap for building a governed SSOT across ERP systems - A sample ERP-focused dbt project to explore and adapt in your own environment **Who should watch** - Finance analytics leaders modernizing ERP reporting - Data and analytics engineers supporting ERP migrations - BI teams struggling with inconsistent ERP metrics - Organizations navigating M&A with multiple ERP systems - Anyone tasked with turning ERP data into reliable strategic insight #### Meet the speakers - Dakota Kelley - Ross McNeely - Stephen Robb #### Host Partner --- --- title: "Modernizing Financial Services: Architecting Governance, Agility, and Trust with dbt" description: "The Guide to Modern Data Transformation in Financial Services" url: "https://www.getdbt.com/resources/webinars/modernizing-financial-services" date: "2026-02-18" categories: ["On-Demand"] --- # Modernizing Financial Services: Architecting Governance, Agility, and Trust with dbt The Guide to Modern Data Transformation in Financial Services Modernization in financial services is rarely a clean break from the past. Legacy architectures, complex cloud migrations, GenAI pressures, and rising regulatory scrutiny (BCBS 239, SOX) mean transformation initiatives must deliver more than speed - they must deliver control, auditability, and measurable business value. A faster data transformation tool isn’t enough. What organizations need is a structural shift in how data is defined, tested, and governed - a software engineering-inspired workflow, and a federated domain model that preserves the big picture. Join us to see why leading global financial institutions are choosing dbt as the standard and how it enables you to decouple business logic from legacy constraints, enforce security best practices, and build the trusted, audit-ready data foundation that AI demands. **Why attend** - Reduce transformation complexity while maintaining regulatory compliance - Lower cloud and warehouse TCO without compromising governance - Introduce CI/CD, automated testing, and version control into data workflows - Enable domain ownership (Data Mesh principles) without losing central oversight - Build a trusted, audit-ready data foundation that AI initiatives can safely rely on - Bridge the gap between business domain experts and engineering teams This webinar focuses on practical architecture and operating model shifts not theory showing how to modernise without increasing risk. **Who should attend** This session is designed for leaders and practitioners within financial services, including: - Chief Data Officers and Heads of Data - Data Platform and Cloud Migration Leaders - Risk, Governance, and Compliance stakeholders - Data Engineering and Analytics Engineering Managers - Enterprise Architects responsible for regulatory alignment If you are accountable for delivering compliant, scalable, AI-ready data platforms in a highly regulated environment, this session is built for you. - Sean McIntyre --- --- title: "dbt Core v1.11 Live: Release updates, roadmap, and Q&A with the dbt Core team" description: "Watch demos of key updates, get roadmap context, and join the live Q&A in this fun, dbt Core release party!" url: "https://www.getdbt.com/resources/webinars/dbt-core-1-11-live-release-updates-roadmap" date: "2026-02-03" categories: ["On-Demand"] --- # dbt Core v1.11 Live: Release updates, roadmap, and Q&A with the dbt Core team Watch demos of key updates, get roadmap context, and join the live Q&A in this fun, dbt Core release party! **Watch the dbt Core team walk through release highlights, demos, discussion, and live community Q&A** dbt Core continues to evolve through steady, thoughtful improvements that make projects more reliable, extensible, and easier to maintain over time. The dbt Core 1.11 GA release includes updates across user-defined functions (UDFs), adapter behavior, and deprecation warnings, along with a set of bug fixes and quality-of-life improvements. This release reflects the ongoing investment in the foundation that so much of the dbt community relies on every day. You’re invited to join members of the dbt Core product, engineering, and developer experience teams for a live virtual event that’s part deep dive, part release party. We’ll walk through what shipped in 1.11, why these changes matter in practice, and how they connect to the broader direction of dbt Core. You’ll see short demos of key updates, get roadmap context, and have plenty of time for live Q&A. **You will learn how to: ** - **Understand what shipped in dbt Core 1.11.** Get a clear overview of the most meaningful updates without digging through release notes. - **Use UDFs more effectively.** Learn how UDF workflows have evolved, see examples in action, and understand what’s coming next. - **Navigate adapter improvements.** See what’s changed across adapters and how those updates affect real-world dbt projects. - **Work with deprecation warnings confidently.** Understand why deprecations exist, how to interpret warnings, and how to future-proof your models. - **See what’s next for dbt.** Get roadmap context and understand how recent releases fit into the longer-term direction. - **Get answers from the Core team.** Ask questions live or hear responses to questions raised by the community ahead of time. **Who should watch:** - Analytics engineers and data engineers using dbt Core - Adapter maintainers and contributors - Teams planning or already upgrading to dbt Core 1.11 - Anyone interested in the future direction of dbt Core #### Meet the speakers ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/9968cfe0792c09611318e72ca19918bec71b413b-1200x300.png) --- --- title: "Modernize Your dbt Project: Upgrade to Fusion in Practice" description: "Upgrade your dbt project to Fusion with confidence. Get a clear, practical path to production." url: "https://www.getdbt.com/resources/webinars/modernize-your-dbt-project-upgrade-to-fusion-in-practice-workshop" date: "2026-02-02" categories: ["On-Demand"] --- # Modernize Your dbt Project: Upgrade to Fusion in Practice Upgrade your dbt project to Fusion with confidence. Get a clear, practical path to production. The dbt Fusion engine is the next generation of dbt: it understands your SQL, catches error earlier, and parses and compiles significantly faster, while staying compatible with your existing dbt Core projects. For teams running **dbt Core** in production, upgrading requires precision. You need to understand how Fusion evaluates your existing project, which dbt Core patterns trigger errors, and how to resolve those issues before enabling Fusion in any environment that matters. This hands-on workshop walks through a real dbt Core to Fusion upgrade from start to finish. You will run the same commands, see the same error classes, and apply the same fixes that surface when teams begin upgrading existing dbt Core projects to Fusion. **What you’ll gain from attending** - **A clear read on your Fusion readiness:** Understand how Fusion parses, compiles, and flags issues in existing dbt Core code. - **Concrete fixes for common upgrade blockers:** Identify hard vs. soft blockers and resolve common problems with `dbt-autofix`. - **A safe, repeatable way to test Fusion before prod:** Walk through a low‑risk validation workflow that uses production artifacts and cloning, so you can try Fusion in a realistic environment without touching your live jobs. **Who should attend this workshop** This session is designed for **current dbt Core users** who: - Own or maintain production dbt Core projects - Work as analytics engineers, platform leads, or data platform admins - Are planning or actively evaluating a Fusion upgrade - Want a confidence and a concrete plan before changing how dbt runs in production #### **Meet your instructor:** - Tyler Rouze --- --- title: "Ready for the dbt Fusion engine? A practical framework for modern data teams" description: "Learn how modern data teams assess Fusion readiness and turn it into a clear plan for evolving their dbt practice." url: "https://www.getdbt.com/resources/webinars/are-you-ready-for-the-dbt-fusion-engine" date: "2026-01-30" categories: ["On-Demand"] --- # Ready for the dbt Fusion engine? A practical framework for modern data teams Learn how modern data teams assess Fusion readiness and turn it into a clear plan for evolving their dbt practice. The dbt Fusion engine changes how dbt works under the hood, bringing development, orchestration, governance, and insights into a single engine. For **dbt Core and dbt platform users**, the question is no longer if Fusion makes sense, but how prepared their existing projects and workflows are to adopt it. But adopting Fusion isn’t just a technical upgrade. It is a chance to step back and examine how your data team builds, governs, and delivers trusted data products today. In this session, **Brooklyn Data** joins us to walk through their Fusion Readiness Assessment, a structured framework designed to help dbt Core and dbt platform users understand what Fusion readiness really looks like and how to evaluate their own starting point. You’ll learn what Fusion enables, why readiness matters, and how teams can prepare their eligible dbt projects to realize value from Fusion without unnecessary risk or disruption. **What you’ll gain from watching** - **What Fusion is and why it matters:** How the dbt Fusion engine changes the way teams build, orchestrate, govern, and analyze data - **How ready you really are:** How project, process, and people readiness affect adoption, using Brooklyn Data’s Fusion Readiness Assessment and real-world examples - **What to do next:** A practical framework to prioritize next steps and accelerate value after Fusion is implemented gaps **Who should watch this workshop** - Analytics engineers - Analytics engineering leaders - Data engineering and platform teams - Heads of data, analytics, or data platforms - Data architects and technical leads - Organizations using dbt Core or dbt Platform who are evaluating or planning for Fusion #### Meet the speakers - Michael Carlone - Aika Zikibayeva --- --- title: "Make your data AI ready with dbt and Snowflake" description: "AI is only as powerful as the meaning behind your data." url: "https://www.getdbt.com/resources/webinars/make-your-data-ai-ready-with-dbt-and-snowflake" date: "2026-01-21" categories: ["On-Demand"] --- # Make your data AI ready with dbt and Snowflake AI is only as powerful as the meaning behind your data. AI is only as powerful as the meaning behind your data. While modern platforms excel at storing and processing information, it’s metadata and semantics that transform raw data into something AI can truly understand, trust, and act on. In this session, we’ll explore how dbt and Snowflake work together to create an AI-ready data foundation by embedding rich business context directly into your data. When you build semantic models in dbt, you’re not just transforming tables, you’re generating the most valuable form of metadata available: documented business logic, data quality signals, and shared definitions that reflect how your organisation actually operates. This semantic layer becomes a critical input for agentic and AI-driven applications, including Snowflake Intelligence. By grounding AI in trusted, well-modelled data, organizations can deliver more accurate and precise responses, dramatically reduce hallucinations, and confidently enable use cases like natural language querying across the enterprise. Whether you’re leading data transformation, scaling analytics, or preparing your platform for AI adoption, this session will show how dbt and Snowflake help turn data into a strategy. **What you’ll learn:** - Why data quality and semantic foundations are essential for AI readiness and successful AI initiatives - How to build unified data pipelines that harmonize structured and unstructured data analysis using dbt and Snowflake Cortex - Practical techniques for incorporating Cortex AI functions (sentiment analysis, summarization, extraction) directly within dbt models - How to create and deploy Snowflake Semantic Views from dbt semantic models for consistent, governed AI consumption - How to expose dbt semantics to AI applications using the dbt MCP Server for natural language analytics - Real-world patterns for enabling Cortex Analyst with rich semantic context from your dbt projects - Best practices for scaling AI adoption while maintaining data trust, quality, and governance **Who should join:** This session is designed for data and technology leaders responsible for building, scaling, and operationalising enterprise data and AI platforms, including: - Chief Data Officers and digital transformation leaders - Data, analytics, and technology leaders - Data and analytics leaders accountable for trusted enterprise metrics - AI, ML, and data science leaders focused on production-ready AI - Enterprise data architects and platform leaders designing governed, scalable foundations - Data engineering and platform teams building on Snowflake - Technology leaders enabling self-service analytics and AI across the business Whether you’re exploring AI use cases, operationalising Snowflake Intelligence, or standardising semantics with dbt, this session is designed to help you deliver more accurate, trusted, and scalable AI outcomes across the enterprise. - Luis Leon - Michael Taylor --- --- title: "Scale dbt without scaling waste: How Fusion cuts redundant model runs" description: "Learn how dbt Fusion helps data teams work smarter, ship faster, and spend less time rebuilding." url: "https://www.getdbt.com/resources/webinars/how-fusion-cuts-redundant-model-runs" date: "2026-01-12" categories: ["On-Demand"] --- # Scale dbt without scaling waste: How Fusion cuts redundant model runs Learn how dbt Fusion helps data teams work smarter, ship faster, and spend less time rebuilding. As dbt projects scale, small changes can trigger full model and test re-runs. That redundant work inflates warehouse spend and pipeline runtimes without improving data quality. Fusion changes the equation. By running only what’s changed and optimizing test execution, teams are seeing 29%+ reductions in warehouse usage while maintaining fast, reliable pipelines. **In this on-demand event, you’ll learn:** - How teams running large dbt projects use state awareness to execute only changed models and their downstream dependencies - How selective test execution reduces unnecessary warehouse compute while preserving data quality guarantees - How eliminating redundant model runs enables teams to reduce warehouse usage by 29% or more **You’ll also hear from [Obie Insurance ](https://www.obieinsurance.com/)and [Analytics8](https://www.analytics8.com/)** on how they’re modernizing with Fusion, including: - How Obie Insurance is achieving approximately 30% model reuse through dbt State, resulting in meaningful compute savings - How both teams are streamlining development workflows and reducing redundant work across large dbt environments - How they’re evaluating Fusion to lower platform costs and simplify data operations ** What else you’ll gain from attending:** - **A demo of Fusion’s cost-saving capabilities**, including: - State-aware orchestration - Efficient testing patterns - Advanced configurations that reduce unnecessary model runs - **Guidance on improving developer efficiency** with the Fusion-powered IDE, lineage, previews, and VS Code extension. - **A first look at cost-optimization features**, including semantic-aware CI and expanded orchestration controls. - **Q&A with a dbt product expert** to help you understand where the biggest cost-savings opportunities are for your team. #### Meet the speakers: - Patrick Vinton - Tyson Dobernek - Matt Karan - Reuben McCreanor --- --- title: "Operationalize analytics agents: dbt AI updates + Mammoth’s AE agent in action" description: "See real examples of AI agents powered by dbt—automating analytics without breaking governance or trust." url: "https://www.getdbt.com/resources/webinars/operationalize-analytics-agents-dbt-ai-updates-mammoth-s-ae-agent-in-action" date: "2026-01-08" categories: ["On-Demand"] --- # Operationalize analytics agents: dbt AI updates + Mammoth’s AE agent in action See real examples of AI agents powered by dbt—automating analytics without breaking governance or trust. Agentic workflows promise faster analytics, but many teams are finding that speed without governance leads to inconsistent results, broken trust, and more rework. When AI agents operate without shared context, quality suffers and delivery slows, rather than accelerating. In this webinar, you’ll see how dbt makes agentic analytics practical, secure, and reliable. Learn how teams are using the now-GA dbt MCP server to enable agent-driven automation across the analytics stack—without sacrificing data quality, governance, or control. You’ll also hear directly from [Mammoth Growth](https://www.mammothgrowth.com/), who will demo what analytics agents look like in real production environments. See how they built an Analytics Engineering (AE) Agent on the dbt MCP server to streamline workflows, reduce manual effort, and ship trusted analytics faster. **What you'll gain from attending** - **Faster analytics delivery without tradeoffs**: Learn how agentic workflows can accelerate development while preserving trust, consistency, and governance - **A clear understanding of what’s available today**: See how the dbt MCP server (GA) powers secure, repeatable analytics agents at scale - **Practical automation examples**: Watch dbt Copilot agents—including the Analyst and Catalog agents (private beta)—in action to reduce manual work and improve consistency - **Real business impact from production use**: See how Mammoth Growth’s AE Agent improves efficiency, quality, and time-to-value—with a demo and concrete takeaways you can apply to your own stack - **A look ahead at what’s coming next**: Get a preview of upcoming Observability and Developer agents designed to further improve speed and reliability #### Meet the speakers - Tom Clinton - Dylan Cruise - Sai Maddali #### Host partner --- --- title: "From Stored Procedures to Scalable Data with Zopa" description: "You’ll learn how Zopa moved from brittle, hard-to-change pipelines to a scalable hub-and-spoke model using dbt Mesh" url: "https://www.getdbt.com/resources/webinars/from-stored-procedures-to-scalable-data" date: "2026-01-08" categories: ["On-Demand"] --- # From Stored Procedures to Scalable Data with Zopa You’ll learn how Zopa moved from brittle, hard-to-change pipelines to a scalable hub-and-spoke model using dbt Mesh As organisations grow, analytics must evolve with them. For Zopa, legacy stored procedures had become a constraint rather than an accelerator. Business logic was opaque, documentation was unreliable, and change depended on a small group of specialists. At the same time, regulatory expectations were increasing and analytics needed to scale beyond a central data team. In this session, Zopa shares how it modernised its analytics stack by moving from stored procedures to an analytics engineering workflow built on dbt. By standardising on modular, tested, version-controlled models with dbt Core, and later adopting the dbt Platform, Zopa reduced risk, improved delivery speed, and enabled governed self-serve analytics across teams. You’ll learn how Zopa moved from brittle, hard-to-change pipelines to a scalable hub-and-spoke model using dbt Mesh. Live documentation, column-level lineage, and built-in CI replaced manual processes and stale Confluence pages, increasing trust in analytics and supporting both product decision-making and regulated reporting. This session is a practical look at how modern analytics engineering enables scale, governance, and confidence in data. **What You’ll Learn** - Why legacy stored procedures limit analytics engineering at scale - How Zopa modernised its analytics workflow using dbt Core with modular models, tests, and version control - How a hub-and-spoke operating model enabled governed self-serve analytics through shared models and contracts - How dbt Platform capabilities such as built-in CI, environments, permissions, and orchestration accelerated delivery - How live documentation and column-level lineage increased trust, adoption, and data literacy across teams - Practical lessons for scaling analytics safely using dbt Mesh and cross-project model reuse **Who Should Attend** - Analytics Engineers and Data Engineers working with dbt - Analytics Engineering Leads and Data Platform Owners - BI and Product Analytics teams adopting governed self-serve analytics - Heads of Data and Analytics - Technology, Risk, and Compliance Leaders supporting regulated analytics use cases - Francis Nwobu - Carlos Castro - Tayo Moore --- --- title: "Closing the context gap: Cribl’s blueprint for trusted AI with dbt + Omni" description: "How Cribl uses dbt + Omni to close the AI context gap—building trusted, governed analytics with a live demo." url: "https://www.getdbt.com/resources/webinars/closing-the-context-gap-cribls-blueprint-for-trusted-ai-with-dbt-omni" date: "2025-12-15" categories: ["On-Demand"] --- # Closing the context gap: Cribl’s blueprint for trusted AI with dbt + Omni How Cribl uses dbt + Omni to close the AI context gap—building trusted, governed analytics with a live demo. Everyone wants AI-driven analytics, but few trust the answers. The problem isn't the AI model; it’s the Context Gap between your data warehouse and the end user. This gap is a disconnect between what a data warehouse can technically answer and the business context, intent, and assumptions the questioner brings with them. When that context is lost, AI outputs are fast, confident, and wrong. Watch and see how modern teams are closing this gap. **Priya Gupta, Head of Data at Cribl**, will share a practical architecture for treating Omni and the dbt Platform as the "Structured Context" engines for the entire business, unifying the lifecycle from code to conversational AI. ** In this session, you will learn how to:** - **Deploy Agentic Modeling:** Use AI agents to "prompt-engineer" dbt descriptions and lineage at scale, automating the creation of a rich Semantic Layer. - **Synchronize the Development Lifecycle:** Stop pushing breaking changes. See a workflow where development happens in dbt and validation happens instantly in Omni—catching errors before merge. ** Plus, a demo of the "AI Context Loop."** We will go under the hood to show: - **Surfacing Metadata:** How connecting MCP servers makes your chatbot an expert in both dbt metadata and Omni analytics. - **The Feedback Loop:** How to use AI-generated answers to audit and improve your upstream dbt documentation, turning user questions into a tool for better governance. **Meet your speakers** - Priya Gupta - Chris Merrick - Kyle Dempsey --- --- title: "Delivering reliable AI with the dbt Semantic Layer and dbt MCP Server" description: "Learn how to govern context, reduce hallucinations, and scale AI with confidence in this live virtual event." url: "https://www.getdbt.com/resources/webinars/delivering-reliable-ai-with-the-dbt-semantic-layer-and-dbt-mcp-server" date: "2025-12-02" categories: ["On-Demand"] --- # Delivering reliable AI with the dbt Semantic Layer and dbt MCP Server Learn how to govern context, reduce hallucinations, and scale AI with confidence in this live virtual event. As teams race to adopt LLMs and agents, many discover the same challenge: AI systems can’t perform well without structured, governed, and consistent context. Critical knowledge—from metrics and definitions to policies, data relationships, and operational rules—often lives in dashboards, documentation, or scattered SQL. Without a shared, authoritative layer of meaning, AI outputs become unreliable and leads to hallucinations, inconsistent answers, and costly rework. In this virtual event, experts from dbt Labs and [phData](https://www.phdata.io/dbt/) will walk through how the dbt Semantic Layer and dbt MCP Server combine to deliver the reliable context AI systems require. You’ll see how the dbt Semantic Layer centralizes and standardizes meaning—and how the dbt MCP Server extends that context directly to AI tools, enabling them to safely discover, query, and reason over trusted, governed data. **What you'll gain from attending** ##### **A clear understanding of the AI context problem** Learn how inconsistently defined data, duplicated logic, and ungoverned SQL make LLMs brittle—causing incorrect answers, unpredictable agent behavior, and unreliable reasoning. Discover how dbt helps teams align on meaning and provide AI systems with trustworthy context by giving AI access to the same governed knowledge your analytics tools rely on. ##### **Demo: AI-ready data in action** See a walkthrough of what’s possible with dbt today, including: - How AI tools use the dbt MCP Server to discover available models, metrics, and metadata - How the agent can ask for context to understand attributes and navigate relationships - How the dbt MCP Server enables AI models to generate governed SQL - How queries can be executed safely using dbt MCP Servers’ built-in guardrails - How consistent semantic context leads to more accurate, predictable, and cost-efficient AI behavior - Plus a quick demo of an agentic chatbot interacting with OpenAI using dbt MCP ##### **Guidance you can use immediately** Leave with practical next steps for getting started with dbt modeling, the dbt Semantic Layer, and dbt MCP Server in your own environment. #### Meet the speakers - Dustin Dorsey - Dakota Kelley - Stephen Robb #### Host partner --- --- title: "Maximizing the business value of your data platform with dbt" description: "Maximize the business value of your data platform with dbt and deliver impact, not just dashboards." url: "https://www.getdbt.com/resources/webinars/maximizing-the-business-value-of-your-data-platform-with-dbt" date: "2025-12-02" categories: ["On-Demand"] --- # Maximizing the business value of your data platform with dbt Maximize the business value of your data platform with dbt and deliver impact, not just dashboards. Even the best data teams can get buried in maintenance—chasing pipeline errors, tuning jobs, or managing platform costs. Meanwhile the real goal gets lost: driving measurable business value. This session will show you how leading data organizations are reclaiming that value. Find out how to spot and eliminate hidden inefficiencies, optimize your data platform with dbt, and translate technical improvements into business outcomes that your executives will care about. In this virtual event, you’ll learn how to: - Cut rework and downtime by reducing data quality issues that cause delays and duplicate effort - Lower your data platform spend with efficient testing and dbt State that eliminate unnecessary runs, saving up to 30% or more - Quantify your ROI using key metrics to demonstrate value to the business - Benchmark your operations with dbt’s Business Value Assessment framework—identifying savings across tooling, IT costs, and business efficiency **Meet your speakers** - David Tishgart - Jamie Nemeroff --- --- title: "Introducing ADE-bench, the world’s first comprehensive benchmark for AI driven analytics and data engineering" description: "Introducing ADE-bench, the world’s first comprehensive benchmark for AI driven analytics and data engineering" url: "https://www.getdbt.com/resources/webinars/analytics-data-engineer-bench" date: "2025-11-13" categories: ["On-Demand"] --- # Introducing ADE-bench, the world’s first comprehensive benchmark for AI driven analytics and data engineering Introducing ADE-bench, the world’s first comprehensive benchmark for AI driven analytics and data engineering So … you’ve probably heard a lot about AI over the last few years. What can it do? What can’t it do? To answer this question - you need to actually put it to the test. That’s why Benn Stancil created the world’s first comprehensive, open source benchmark for analytics and data engineering work (ADE-bench). Discover how ADE-bench measures real-world analytics engineering tasks for AI-assisted workflows, why it matters for teams building with dbt, and how to run it yourself. Featuring benchmark creator Benn Stancil, this session will unpack the design, demo the workflow, and share early takeaways to help you adopt the Model Context Protocol (MCP) with confidence. - **Who should watch** - Analytics engineers - Data engineers - AI engineers and technical decision makers - **Why watch** - Elevate your team’s approach to AI-assisted data development - Get hands-on with a credible, open benchmark you can rerun and extend - See how to quantify improvements and share results with your org and the community #### Meet the speakers: - Benn Stancil - Jason Ganz --- --- title: "dbt Fusion engine on-demand demo" description: "Discover how the dbt Fusion engine speeds up SQL development with instant validation, smarter runs, and lower warehouse costs." url: "https://www.getdbt.com/resources/webinars/dbt-fusion-engine-on-demand-demo" date: "2025-11-11" categories: ["On-Demand"] --- # dbt Fusion engine on-demand demo Discover how the dbt Fusion engine speeds up SQL development with instant validation, smarter runs, and lower warehouse costs. When you make a small SQL change, you shouldn’t have to rerun your entire project or wait for the warehouse to confirm what you already suspect. The **dbt Fusion engine** changes that. It understands your SQL before it runs, validates code instantly, and only executes what’s actually changed. The result is faster iteration, fewer wasted cycles, and lower warehouse costs—without sacrificing accuracy or confidence. In this on-demand demo, you’ll see how the **dbt Fusion engine** transforms the development process. Watch how its Rust-based architecture improves performance, how real-time validation in VS Code catches issues before they reach production, and how state-aware orchestration intelligently skips redundant runs. You’ll also see what the new dbt Studio experience looks like with the **dbt Fusion engine** at its core, built to help you move quickly from local development to production-ready data models. **You will learn how to ** - **Start quickly in VS Code.** Install the official dbt extension and upgrade existing projects with dbt autofix. - **Validate SQL locally—instantly.** Catch typos, syntax errors, and dialect mismatches as you type, without burning warehouse compute. - **Preview CTEs, refactor safely, and navigate lineage.** See CTE outputs inline, rename models/columns with downstream updates, and inspect column‑level lineage as you code. - **Work across leading warehouses.** Develop with Fusion against Snowflake, Databricks, BigQuery, and Redshift. - **Run smarter with dbt State.** Skip unchanged models, prioritize freshness SLAs, and minimize redundant tests. **Who should watch ** - Data practitioners - Data engineers - Analytics engineers - Solution architects #### Meet our presenter: - Sara Gawlinski --- --- title: "Smarter pipelines, 29%+ warehouse savings: How the dbt Fusion engine drives more cost-effective data ops" description: "Learn how how dbt Fusion helps data teams run smarter, deliver faster, and spend less time rebuilding." url: "https://www.getdbt.com/resources/webinars/how-the-dbt-fusion-engine-optimizes-data-work" date: "2025-10-27" categories: ["On-Demand"] --- # Smarter pipelines, 29%+ warehouse savings: How the dbt Fusion engine drives more cost-effective data ops Learn how how dbt Fusion helps data teams run smarter, deliver faster, and spend less time rebuilding. Data teams waste hundreds of hours every year refreshing models that haven’t changed. This is a significant time and trust drain on practitioners, and it can unnecessarily drive data platform costs through the roof. The dbt Fusion engine changes that equation. In testing, it’s delivered **29%+ reductions in warehouse usage** by running only what’s changed, optimizing test execution, and using advanced configurations to eliminate unnecessary model runs — cutting wasted compute without compromising freshness or reliability. **In this virtual event, you’ll learn how teams are using Fusion to:** - Significantly reduce warehouse and platform costs by eliminating redundant work - Run pipelines that are leaner, faster, and more predictable - Deliver more reliable data through simplified, centralized orchestration - Improve the developer experience with faster iterations, better debugging, and clearer lineage - Support business growth with smarter build strategies that scale **You’ll also hear from [Obie Insurance ](https://www.obieinsurance.com/)and [Analytics8](https://www.analytics8.com/) as they share how they’re modernizing with Fusion, including how:** - Obie is seeing ~30% model reuse with dbt State — resulting in meaningful compute savings - Both teams are streamlining development and reducing redundant work - They’re exploring Fusion as a way to lower platform costs and simplify their data operations ** What else you’ll gain from attending:** - **A demo of Fusion’s cost-saving capabilities**, including: - State-aware orchestration - Efficient testing patterns - Advanced configurations that reduce unnecessary model runs - **Guidance on improving developer efficiency** with the Fusion-powered IDE, lineage, previews, and VS Code extension. - **A look ahead at upcoming cost-optimization features**, including semantic-aware CI and expanded orchestration controls. - **Q&A with a dbt product expert** to help you understand where the biggest cost-savings opportunities are for your team. #### Meet the speakers: - Patrick Vinton - Tyson Dobernek - Matt Karan - Reuben McCreanor --- --- title: "Speed, simplicity, cost savings: Experience the dbt Fusion engine" description: "Learn how to end fragile pipelines, cut cloud compute costs, and simplify workflows for both dbt Core and dbt platform users." url: "https://www.getdbt.com/resources/webinars/speed-simplicity-cost-savings-experience-the-dbt-fusion-engine" date: "2025-10-06" categories: ["On-Demand"] --- # Speed, simplicity, cost savings: Experience the dbt Fusion engine Learn how to end fragile pipelines, cut cloud compute costs, and simplify workflows for both dbt Core and dbt platform users. As analytics projects grow, so do the challenges: fragile pipelines, rising compute costs, and slow, error-prone development cycles. For [Sonja Strempel](https://www.linkedin.com/in/sonjastrempel/), Analytics Engineer at DPG Media, the dbt Fusion engine has been a game-changer. In this live virtual event, Sonja joins dbt Labs founder & CEO, [Tristan Handy](https://www.linkedin.com/in/tristanhandy/) and product manager, [Elias DeFaria](https://www.linkedin.com/in/eliasdefaria/) to share how Fusion is reshaping her daily work — helping her build faster, collaborate more effectively, and deliver trustworthy data to stakeholders. **You’ll hear firsthand how Sonja uses Fusion to:** - Catch errors instantly with live feedback and in-line Intellisense - Build confidence in her models using column-level lineage - Speed development and increase efficiency with the VS Code extension - Strengthen trust in data quality — leading to faster, more confident decisions across her team Whether you’re working locally in dbt Core or scaling enterprise analytics in the dbt platform, register now to see how the dbt Fusion engine helps you spend less time firefighting and more time delivering insights. **What you’ll gain from watching** - A demo showing new development experiences, including dbt compare and large-scale project performance - Real-world insights from **Sonja Strempel** on how Fusion improves collaboration and trust - Guidance on how Fusion improves the developer experience across dbt Core and the dbt platform - A preview of upcoming dbt Fusion engine features and what’s next for dbt users - Get your questions answered live from a dbt product expert #### Meet the speakers - Tristan Handy - Elias DeFaria - Sonja Strempel #### Host sponsor --- --- title: "Insights from Databricks & dbt Labs data leaders: Scaling reliable analytics in the AI era" description: "Databricks and dbt Labs share strategies to balance AI hype with reliable, trusted analytics." url: "https://www.getdbt.com/resources/webinars/insights-from-data-leaders-scaling-reliable-analytics-in-the-ai-era" date: "2025-09-22" categories: ["On-Demand"] --- # Insights from Databricks & dbt Labs data leaders: Scaling reliable analytics in the AI era Databricks and dbt Labs share strategies to balance AI hype with reliable, trusted analytics. The future of analytics hinges on bridging the gap between AI hype and tangible business impact. Leaders expect measurable ROI from AI initiatives, stakeholders expect instant trusted insights, and data teams still face fragile pipelines and governance gaps. dbt Labs’ annual [State of Analytics Engineering report](https://www.getdbt.com/resources/state-of-analytics-engineering-2025) highlighted the durable trends shaping data teams. In the months since, LLMs, AI-assisted coding, and [Databricks](https://www.databricks.com/) adoption have only accelerated, intensifying the pressure. The question now is: _what does the future of data teams look like in an AI-powered era?_ Watch two of the best minds in data and AI, [David Totten](https://www.linkedin.com/in/davidwtotten/) (AVP Field Engineering, Databricks) and [Ryan Segar](https://www.linkedin.com/in/ryan-segar-50537849/) (Chief Product Officer, dbt Labs), for a candid conversation on the state of data and AI today and where we’re headed next. **What you will hear them explore in this virtual event** - How data leaders can balance AI hype with durable trends that actually deliver business value - Why the trust gap in data persists and how teams can finally close it - Where dbt and Databricks are partnering to help teams scale analytics and AI responsibly - What the future of analytics engineering looks like in an AI-powered era Don’t miss this chance to hear directly from two leaders shaping the future of data and AI. Their insights will help guide your team’s strategy for the year ahead. ### Meet the speakers - David Totten - Ryan Segar ### Host partner ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/1c9bbaec396fc4bd7371e4287ee0ad577cd550cf-3999x634.png) --- --- title: "Talk to your data: AI-powered conversational analytics with the dbt MCP server" description: "Ask a question and get a trusted answer. See how Norlys made AI-powered analytics actually work, governed and fast." url: "https://www.getdbt.com/resources/webinars/ai-powered-conversational-analytics-with-the-dbt-mcp-server" date: "2025-09-10" categories: ["On-Demand"] --- # Talk to your data: AI-powered conversational analytics with the dbt MCP server Ask a question and get a trusted answer. See how Norlys made AI-powered analytics actually work, governed and fast. Ask three different chatbots for “revenue this quarter,” and you may get three different numbers. Trust erodes, adoption stalls, and your data team inherits the mess. Without shared definitions and lineage, chat interfaces can create costly rework and compliance risk. If you can’t trace the metric and see how it was produced, you probably shouldn’t trust it. That’s the challenge [Norlys](https://norlys.dk/), Denmark’s largest energy-telecom group, set out to solve with [LEAP](https://www.leap-consulting.dk/) Data & AI Consulting**.** Together, they designed and launched a production-ready conversational analytics tool powered by the dbt Model Context Protocol (MCP) server and the dbt Semantic Layer. By exposing definitions, tests, and lineage to their AI system, Norlys delivers **fast, verifiable self-service insights** to every business user—without creating new risks for the data team. Whether you’re exploring AI use cases or preparing to ship conversational analytics into production, this session offers a practical playbook for turning today’s hype into real business value. **What you’ll gain from watching:** - **Building reliable AI chat experiences:** Practical lessons from Norlys and LEAP on designing a conversational analytics interface that works in production - **See it in action:** A live demo of Norlys’ conversational analytics experience—how plain-English questions become accurate, trusted answers - **Operational takeaways:** Guidance on scalability, reliability, and integration considerations for AI-powered BI - **Team impact:** How conversational analytics reduces analyst workload and expands business access to data - **Next steps:** Where to start if you want to experiment with the dbt MCP Server and the dbt Semantic Layer in your own stack - **Live Q&A:** Get your questions answered by experts from dbt Labs and LEAP Consulting #### **Meet your speakers:** - Søren Meincke Persson - Jonas Munk - Thomas Grabowski #### **Host partners:** --- --- title: "dbt on-demand demo: Built for the next era of analytics" description: "Learn how dbt helps you build trusted data pipelines, accelerate analytics, and maintain data quality at scale." url: "https://www.getdbt.com/resources/webinars/dbt-platform-on-demand-demo" date: "2025-09-09" categories: ["On-Demand"] --- # dbt on-demand demo: Built for the next era of analytics Learn how dbt helps you build trusted data pipelines, accelerate analytics, and maintain data quality at scale. Curious about how dbt can simplify your data workflows? Our 30-minute on-demand demo gives you a walkthrough of how dbt helps you build trusted data pipelines, accelerate analytics, and maintain data quality at scale. You’ll also see the dbt Fusion engine in action - powering smarter, faster development in both the dbt VS Code extension and across the dbt platform. Watch now to see how the dbt platform helps you lay the trusted data foundation that modern analytics and AI demand. **You will learn how to ** **Discover:** - Find and navigate resources in dbt Catalog, understand relationships with project and column-level lineage, and view model query history and data-health signals so you can quickly assess quality. - Use exposures to quickly identify the downstream use of your dbt project, such as in a dashboard, application, or data science pipeline. **Develop:** - Build in your preferred environment with the Studio IDE or the official dbt VS Code extension powered by Fusion. - Increase confidence in your data with built-in testing and documentation. **Collaborate:** - Empower more collaborators with dbt Canvas and dbt Insights. - Align analytics work with consistent metrics powered by the dbt Semantic Layer. **Operate:** - Schedule and orchestrate pipelines with built-in job scheduling and orchestration - plus logging and alerting. - Observe and manage spend with cost management dashboards. **AI & Automation:** - Build AI-ready workflows with the dbt MCP server, making trusted data models accessible to intelligent systems. - Streamline repetitive work with dbt Copilot for test, documentation, and SQL generation. **Who should watch ** - Data practitioners - Data engineers - Analytics engineers - Solution architects #### Meet our presenter: - Sara Gawlinski --- --- title: "dbt MCP Server Office Hours — Integrating AI and dbt" description: "Join us for an interactive dbt Community webinar, where we explore the powerful emerging paradigm shaping AI-agentic workflows" url: "https://www.getdbt.com/resources/webinars/dbt-mcp-server-office-hours-integrating-ai-and-dbt" date: "2025-09-08" categories: ["On-Demand"] --- # dbt MCP Server Office Hours — Integrating AI and dbt Join us for an interactive dbt Community webinar, where we explore the powerful emerging paradigm shaping AI-agentic workflows Watch our interactive dbt Community Webinar where we explore the powerful emerging paradigm shaping AI-agentic workflows: [the dbt Model Context Protocol (MCP) Server](https://docs.getdbt.com/docs/dbt-ai/about-mcp). This is your chance to dive deep, share hands-on experiences, ask questions, and provide feedback as we collectively shape the future of AI-driven data workflows within the dbt ecosystem. **What is the dbt MCP Server?** The dbt MCP Server implements the Model Context Protocol, enabling AI systems—such as language models and autonomous agents—to access trusted, structured context from your dbt projects: models, metrics, lineage, and compute operations. **** **With MCP, AI agents can:** - **Discover data assets:** Query your model definitions, lineage, and metadata for full visibility into your data landscape . - **Access governed metrics**: Leverage dbt’s Semantic Layer to retrieve trusted metrics (e.g., monthly revenue) directly aligned with your organizational truth . - **Improve day to day tasks:** Get run results from your dbt jobs, trigger a job from your development environment - **Trigger dbt development operations:** Execute CLI tasks—run, test, compile, build—via conversational or agentic workflows . **** **Why Participate?** - **Shape the future:** Provide input on how the dbt MCP server should evolve, helping build a safer, smarter standard for AI-data integration. - **Solve common challenges:** Discuss and share strategies overcoming issues around deployment, governance, permissions, and tooling maturity . - **Real-world use cases:** Learn what’s possible—from self-service analytics and conversational data exploration, to agent-powered migration workflows that reduce migration timelines. **** **Session Format** - **Live Q&A:** Bring your questions—whether technical, strategic, or visionary. - **Use‑case highlight:** We’ll step through live examples and workflows showcasing discovery, semantic querying, and project execution. - **Open discussion**: Share your current or potential use cases, hear from others in the community, and help shape the MCP roadmap. **** **Discussion Points:** - Setting up and deploying the local vs. remote MCP Server . - Integrations with clients like Cursor, Claude Desktop, VS Code, and other AI tools . - Building AI agents that can understand, query, and act upon your dbt context with confidence and consistency . Let’s co-create a space where human expertise meets AI, powered by transparent metrics, lineage awareness, and conversation with your data. Whether you’re a seasoned dbt user and are using agentic workflows on a daily basis, or you are learning about the dbt MCP server for the first time—this is your invitation to shape the future of intelligent, trustworthy data systems. **What you’ll gain from watching** - A clear understanding of why AI agents need more than just access to raw data - How the dbt MCP server adds lineage, logic, and governance, so agents can act with confidence - A behind-the-scenes look at Indicium’s agent that automates and validates dbt migrations at scale - Inspiration for building your own intelligent agents and automated workflows ### Meet our host: [Benoit Perigaud](https://www.linkedin.com/in/benoit-perigaud/), Staff Developer Experience Advocate, dbt Labs Benoit is a Staff Developer Experience Advocate based in Madrid, Spain. Prior to joining dbt Labs, he was an active dbt practitioner in his previous role, gaining hands-on experience with the framework. During his three-year tenure on the Professional Services team at dbt Labs, he specialized in helping customers maximize the value of their dbt implementations. He has now transitioned to the Developer Experience team, where he continues to enhance the dbt ecosystem for developers worldwide. ### --- --- title: "From Informatica to dbt: A migration path to an AI-ready data control plane" description: "Informatica slows you down. dbt delivers faster, cheaper, AI-ready analytics with governed, flexible pipelines." url: "https://www.getdbt.com/resources/webinars/from-informatica-to-dbt-a-migration-path-to-an-ai-ready-data-control-plane" date: "2025-09-05" categories: ["On-Demand"] --- # From Informatica to dbt: A migration path to an AI-ready data control plane Informatica slows you down. dbt delivers faster, cheaper, AI-ready analytics with governed, flexible pipelines. Legacy ETL tools weren’t built for today’s data teams. If you’re still on Informatica, you’re paying too much, moving too slow, and struggling to get your stack ready for AI. It doesn’t have to be that way. Join us to see how companies are moving off Informatica and onto a modern, cloud-native approach with dbt. Learn how Infinite Lambda's Flowline modernizes legacy Informatica ETL to an AI-ready data platform in weeks, slashing license costs and accelerating time-to-value. **What you'll dive into** - Why Informatica’s complexity and licensing model keep teams stuck in the past - How to split ingestion from transformation and accelerate pipelines with dbt Platform - Real-world examples of companies cutting costs and delivering insights faster **Who should attend** - Data leaders looking to modernize their stack and reduce spend - Teams running Informatica who want a clear migration path - Practitioners who want faster, governed, version-controlled workflows The cloud has changed everything. Sticking with Informatica means slow pipelines, higher costs, and missed opportunities. dbt helps you deliver analytics faster, build governed, AI-ready pipelines, and ensure flexibility across your data platform. See why Informatica customers are choosing dbt for the next era of analytics. **Meet your speakers** - Sara Gawlinski - Petyo Pahunchev --- --- title: "Modeling for success: Building data structures that last" description: "Avoid costly mistakes—join dbt experts to learn data modeling practices that scale with your business." url: "https://www.getdbt.com/resources/webinars/modeling-for-success-building-data-structures-that-last" date: "2025-09-02" categories: ["On-Demand"] --- # Modeling for success: Building data structures that last Avoid costly mistakes—join dbt experts to learn data modeling practices that scale with your business. Poor data modeling is one of the most common and costly mistakes teams make when implementing dbt. Quick fixes and one-off designs may seem faster in the moment, but they create hidden risks: redundant code, fragile pipelines, and data that’s hard to trust or scale. A well-designed model is the foundation for sustainable analytics. Join experts from dbt Labs and [phData](https://www.phdata.io/dbt/) as they share proven strategies for structuring models that align to business processes, reduce complexity, and keep projects future-ready. You’ll learn why dimensional modeling is the most effective approach for dbt’s batch-oriented workflows, and how intentional design pays dividends in speed, cost, and trust. **What you’ll gain from attending** - **Clarity on modeling approaches:** Compare dimensional, data vault, relational, and one-big-table models, and learn when each makes sense - **Best practices in action:** See examples of good and bad modeling decisions, and their impact on scalability, cost, and governance - **A blueprint for success:** Learn how to translate business requirements into technical designs that evolve with your needs - **Real-world insights:** Hear stories from experts who’ve guided dozens of dbt implementations—what works, what doesn’t, and why modeling right matters most - **Get your questions answered live** by a dbt product expert **** #### **Meet your speakers:** - Dustin Dorsey - Dakota Kelley - Stephen Robb #### **Host partner:** --- --- title: "Fast track to dbt workshop" description: "Led by a live instructor, this fast-paced workshop is designed for SQL users who want to stop guessing and start building" url: "https://www.getdbt.com/resources/webinars/fast-track-to-dbt-workshop" date: "2025-07-28" categories: ["Upcoming"] --- # Fast track to dbt workshop Led by a live instructor, this fast-paced workshop is designed for SQL users who want to stop guessing and start building #### **📍 Free, live virtual workshop** #### Wednesday, September 9, 2026 1:00 PM - 3:00 PM CT Ready to fast track your way into dbt? Whether you're building reports, cleaning up messy data, or exploring new tools to streamline your workflow, this hands-on workshop is the perfect place to start. In just two hours, you'll get a guided introduction to the dbt platform, build a working project in Snowflake, and explore tools like dbt Wizard, an AI-powered assistant purpose-built for dbt workflows, and dbt Catalog, your window into the structure and status of your data models. Led by an instructor, with a technical assistant answering your questions in real time, this fast-paced workshop is designed for SQL users who want to stop guessing and start building. You'll be working on Fusion, the dbt platform's engine — the same one now powering the newly open-sourced dbt Core v2.0. It's one of the biggest shifts in dbt's history, and you'll be building on it from day one. By the end, you'll walk away with a working dbt project, a solid understanding of the dbt basics, and ideas for how to use dbt in your own work. **What you’ll gain from attending** - Understand what dbt is and how it fits into your existing workflow - Build and run your first dbt models - Use dbt Wizard to understand, improve, and troubleshoot dbt projects - Navigate your project with ease using dbt Catalog - Test, document, and deploy your data transformations - Get hands-on with Fusion in the dbt platform, a full superset of dbt Core v2.0 - Get your questions answered live by a dbt product expert **Who should attend this workshop** - Data practitioners - Data engineers - Data analysts - Analytics engineers - Solution architects #### **Meet your instructor:** - Shania Thomas --- --- title: "ETL is over: Faster, smarter pipelines with dbt" description: "See how a global travel brand modernized from legacy ETL to cloud native ELT with demos, migration tips, and AI-ready pipelines." url: "https://www.getdbt.com/resources/webinars/etl-is-over-faster-smarter-pipelines-with-dbt" date: "2025-07-25" categories: ["On-Demand"] --- # ETL is over: Faster, smarter pipelines with dbt See how a global travel brand modernized from legacy ETL to cloud native ELT with demos, migration tips, and AI-ready pipelines. #### **Still stuck in the ETL era?** You’re not alone, but the smartest teams are already moving on. Legacy ETL tools weren’t built for the cloud, and they’re holding your data team back. From bloated pipelines to rising costs, it’s getting harder to scale, move fast, or stay competitive. In this on-demand webinar, learn how a leading global travel brand partnered with [Tredence](https://www.tredence.com/partners) and dbt Labs to modernize their data architecture by moving from legacy ETL to a cloud-native ELT stack that **delivers speed, flexibility, and AI-readiness.** You’ll see **a demo of an automated ETL migration tool**, **an inside look at the migration process, and learn practical strategies** you can take back to your stack. Don’t get left behind - see how the move from ETL to ELT is reshaping what’s possible for modern data teams. **What you'll gain from attending** - See what “modern ELT” really looks like: from ingestion to transformation to version control - Learn how to accelerate your own migration from legacy tools with support from Tredence and dbt Labs - Strategies to speed up pipelines, reduce costs, and prepare your pipelines for AI ### **Meet our speakers** - Ernesto Ongaro - Devang Pandya --- --- title: "From stored procedures to dbt: Your playbook for successful migrations" description: "Learn how organizations are reducing maintenance headaches, boosting trust, and accelerating delivery." url: "https://www.getdbt.com/resources/webinars/from-stored-procedures-to-dbt-your-playbook-for-successful-migrations" date: "2025-07-03" categories: ["On-Demand"] --- # From stored procedures to dbt: Your playbook for successful migrations Learn how organizations are reducing maintenance headaches, boosting trust, and accelerating delivery. Stored procedures were built for a different era. They’re often opaque, hard to maintain, and too rigid to keep up with today’s demand for fast, reliable insights. As data teams face growing pressure, these legacy approaches lead to hidden risks: limited visibility, manual workarounds, and pipelines that don’t scale. The dbt platform offers a modern alternative that’s **modular, scalable, and built for collaboration**. Join experts from dbt Labs and [phData](https://www.phdata.io/dbt/) to see how teams are replacing brittle stored procedures with transparent, scalable dbt models. You’ll hear real-world examples and learn how organizations are **reducing maintenance headaches, boosting trust, and accelerating delivery.** **What you'll gain from watching** - **Clarity on why stored procedures fall short:** Learn how it impacts your team’s speed and trust in data - **Side-by-side demo:** See a stored proc rebuilt in dbt, plus added benefits like testing, logging, and version control - **A practical migration playbook:** Learn how to assess, prioritize, and execute your move from stored procs to dbt, with real-world tips on modularization, macros, and validation. - **Real-world insights:** Hear how a customer tackled their migration to improve speed, governance, and maintainability. #### Meet our speakers: ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/6b8e68eb7de8e771927bd5c1b7235febb491c9b2-1800x900.png) #### Host partner: --- --- title: "Build reliable AI agents with the dbt MCP server" description: "AI agents need context. Learn how the dbt MCP server adds structure and how Indicium cuts migration time by 90%." url: "https://www.getdbt.com/resources/webinars/build-reliable-ai-agents-with-the-dbt-mcp-server" date: "2025-06-24" categories: ["On-Demand"] --- # Build reliable AI agents with the dbt MCP server AI agents need context. Learn how the dbt MCP server adds structure and how Indicium cuts migration time by 90%. AI agents are evolving beyond chat, but too many still rely on raw data without the context needed for safe, scalable automation. Without structure and governance, agents hallucinate, workflows break, and trust erodes. dbt and the dbt MCP server solve this by making trusted data models accessible to intelligent systems. Join dbt Labs and [Indicium](https://indicium.tech/) for a live session on how teams are building reliable production-grade AI agents, grounded in governed, structured context. You’ll learn how dbt and the new dbt Model Context Protocol (MCP) server are powering **intelligent workflows** that are **reliable, secure, and scalable by design.** This is the third episode in our [2025 dbt Launch Showcase](https://www.getdbt.com/resources/webinars/2025-dbt-cloud-launch-showcase) series—spotlighting the foundations of the new analytics and AI ecosystem. In this session, we’ll share how Indicium is using large language models, knowledge graphs, and the dbt MCP server to build a migration agent that **cuts legacy-to-dbt timelines by up to 90%.** **What you’ll gain from watching** - A clear understanding of why AI agents need more than just access to raw data - How the dbt MCP server adds lineage, logic, and governance, so agents can act with confidence - A behind-the-scenes look at Indicium’s agent that automates and validates dbt migrations at scale - Inspiration for building your own intelligent agents and automated workflows ### Meet our speakers ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/12b4c350c1937e94954f8a5257ade428517d3010-1800x600.png) ### Host partner ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/37e6627d59cee8a90795e607c3d33023b2a49257-1467x462.png) --- --- title: "Partner Quarterly Session - July (APJ)" description: "Please join us for our inaugural APJ Partner Updates Webinar - “dbt Run-down”" url: "https://www.getdbt.com/resources/webinars/q2fy26-partner-quarterly-session-apj" date: "2025-06-10" categories: ["On-Demand"] --- # Partner Quarterly Session - July (APJ) Please join us for our inaugural APJ Partner Updates Webinar - “dbt Run-down” Please join us for our inaugural APJ Partner Updates Webinar - “dbt Run-down”. The intent of the session is to keep you informed about all things dbt; this particular session will feature product updates and announcements from our May Launch Showcase. ### **Agenda:** - Welcome/Introduction - Aaron McGrath - 5 min - May Launch Showcase Recap - Stephen Robb - 40 min - Customer Success story - Craig Lowe - 10 min --- --- title: "2025 State of Analytics Engineering" description: "Explore evolving data roles, Gen AI impacts, and data quality trends with experts unpacking the 2025 analytics landscape." url: "https://www.getdbt.com/resources/webinars/2025-state-of-analytics-engineering-virtual-event" date: "2025-04-08" categories: ["On-Demand"] --- # 2025 State of Analytics Engineering Explore evolving data roles, Gen AI impacts, and data quality trends with experts unpacking the 2025 analytics landscape. The results are in! It’s time to interpret them. The [2025 State of Analytics Engineering report](https://www.getdbt.com/resources/reports/state-of-analytics-engineering-2025) is out, capturing the pulse of a fast-moving ecosystem with hard data. But what do these findings mean for real-world data teams? Watch a dynamic discussion with three top minds in data and analytics: **Yannick Misteli** (Roche), **Jenna Jordan** (Analytics8), and **Jason Ganz** (dbt Labs). Expect strong opinions, real-world stories, and an honest look at the trends that matter. Whether you’re rethinking your org structure or navigating the latest in AI, you’ll walk away with a deeper, more grounded understanding of where the industry is headed. **What you can expect to see covered** - **Analytics Engineering Craft**: What’s changed since last year and what hasn’t? How are roles evolving, and where is growth happening? - **AI**: Semantic layers, natural language interfaces, and dare we say agents? How do we bridge the gap between experimentation and real impact? - **Data Quality**: Why it’s still the #1 challenge and what it might actually take to fix it. #### Meet the speakers: - Yannick Misteli - Jenna Jordan - Jason Ganz --- --- title: "dbt + BigQuery: What’s new from Google Cloud Next 25" description: "Learn about the latest from Google Next, what we announced and how you can take your data work to the next level!" url: "https://www.getdbt.com/resources/webinars/dbt-bigquery-whats-new-from-google-next" date: "2025-04-02" categories: ["On-Demand"] --- # dbt + BigQuery: What’s new from Google Cloud Next 25 Learn about the latest from Google Next, what we announced and how you can take your data work to the next level! **BIG NEWS: dbt Cloud lands on Google Cloud! **Your data control plane and platform can now live together in harmony on Google's trusted infrastructure. Check out our webinar with the Google Cloud team where we unveiled everything new in this game-changing partnership. We shared demos, tips, and insider knowledge to help you supercharge your data strategy. Whether you're a BigQuery power user or starting out, explore how this integration will transform the way you build and scale data and AI environments. **What You'll Learn** - **Deploy smarter, not harder: **Level-up flexibility and compliance with native Google Cloud deployment. - **BigQuery + DataFrame magic: **See the BigQuery DataFrame integration that's changing the game. - **Speed + scale up: **Proven tactics for building lightning-fast BigQuery pipelines with dbt Cloud. - **Success secrets:** Get a peek at how cutting-edge customers are using this integration for big real-world wins! #### Meet our presenters - Jobin George - Sandeep Karmarkar - Sai Maddali --- --- title: "From cost center to profit driver: how modern teams optimize data costs" description: "Learn how data leaders optimize costs—fast, practical strategies inside." url: "https://www.getdbt.com/resources/webinars/from-cost-center-to-profit-driver" date: "2025-04-02" categories: ["On-Demand"] --- # From cost center to profit driver: how modern teams optimize data costs Learn how data leaders optimize costs—fast, practical strategies inside. Want to turn your data team from a cost center into a revenue engine, but not sure where to start? Check out our on-demand webinar to learn how top data leaders are shifting from cost center to strategic growth engine— and how you can too. **** #### **You’ll Learn How To:** - Uncover revenue opportunities and real-world success stories - Streamline workflows to cut costs and boost efficiency - Leverage analytics to drive smarter, faster business growth **** #### **Meet the Speakers** - [**Ernesto Ongaro**](https://www.linkedin.com/in/eongaro/), Staff Product Marketing Manager @ dbt Labs - [**Ben Kramer**](https://www.linkedin.com/in/ben-kramer-data/), Senior Director, Data Analytics @ BILT - [**Collin Lenon**](https://www.linkedin.com/in/collin-lenon/), Senior Analytics Engineer, Data Platform @ ClickUp We’ll send the recording, plus all the expert insights, straight to your inbox. --- --- title: "The next era of analytics: A discussion with the CTO" description: "Watch our Chief Technology Officer share how dbt helps teams embrace analytics best practices." url: "https://www.getdbt.com/resources/webinars/ask-the-cto-virtual-event" date: "2025-02-20" categories: ["On-Demand"] --- # The next era of analytics: A discussion with the CTO Watch our Chief Technology Officer share how dbt helps teams embrace analytics best practices. Secular shifts like AI, self-service analytics, and the imperative to translate data into strategic advantage make high-quality data more important than ever before. Organizations need standardized, scalable ways to ensure that data teams and the business stakeholders they support can build and leverage reliable data products that are consumption and AI-ready. With data coming from a myriad of places and platforms and being consumed by an ever-growing audience of consumers…this is no small task. dbt has a robust and aggressive product roadmap designed to support our customers in this journey…and we’re only getting started. **Watch our Chief Technology Officer, Mark Porter, share more about how dbt is innovating to help customers embrace analytics best practices in the modern era.** Don’t miss his point of view on how our recent acquisition of SDF Labs will usher in a step-function improvement to all dbt workflows, empower our users, and push the horizons for analytics. **What you can expect:** - **Data maturity and AI: **Use cases for generative AI are fast-growing and they all rely on trusted data. Discover how to mature your data practices to build trusted, reliable data products that are AI-ready. - **dbt and SDF: **Learn how bringing these two technologies together will improve developer experience, optimize data costs, and cement dbt as the best platform to standardize analytics workflows. - **Interactive Q&A: **Mark answers live and pre-submitted questions from our live event registrants. - Mark Porter - Roxi Dahlke --- --- title: "dbt Developer Day" description: "Learn about the new and coming-soon features that will accelerate data development for practitioners in dbt." url: "https://www.getdbt.com/resources/webinars/dbt-developer-day" date: "2025-02-19" categories: ["On-Demand"] --- # dbt Developer Day Learn about the new and coming-soon features that will accelerate data development for practitioners in dbt. dbt was built by and for data developers. While our platform has expanded to bring more data collaborators into the analytics workflow, our #1 commitment remains firm: to maintain dbt as the best place for data developers to do their work. A huge factor in delivering on that commitment is making development in dbt as snappy, responsive, and fast as humanly possible. **Watch our on-demand virtual event to see the new and coming-soon features that will accelerate data development for practitioners building data models in dbt.** The best part? Speed and correctness don’t need to be a tradeoff. dbt makes it turnkey to develop analytics products fast, without compromising quality. Let us show you how. **What you can expect:** - **SDF integration demo:** See the latest demo on our SDF integration, featuring lightning-fast parse times and IntelliSense features that ensure you’re shipping accurate code. - **dbt Copilot demo:** Discover how AI can accelerate your productivity and help you minimize low-value work. - **dbt Core v1.10 update:** Get a sneak peek at what’s coming in dbt Core v1.10, including the ability to run faster in development and CI with simple time-based sampling. - **Interactive Q&A:** This is your chance to get direct answers from the product experts working on these innovations. Bring your questions and join the conversation. **Meet our speakers:** - Jeremy Cohen - Grace Goheen - Elias DeFaria - Alexis Jones --- --- title: "2025 dbt Launch Showcase" description: "Empowering the next era of analytics. Join us to hear from our leaders about the latest features landing in dbt." url: "https://www.getdbt.com/resources/webinars/2025-dbt-cloud-launch-showcase" date: "2025-01-24" categories: ["On-Demand"] --- # 2025 dbt Launch Showcase Empowering the next era of analytics. Join us to hear from our leaders about the latest features landing in dbt. ### On-Demand Now **_Empowering the next era of analytics_** _The work of analytics is evolving in the AI era, and organizations need a scalable foundation to empower teams to tackle this next wave with confidence and agility. Whether you’re a data practitioner building and deploying pipelines, an analyst imparting your business knowledge into them, or an executive concerned with future-proofing your data platform investments, **new features in dbt empower our customers like never before to scale analytics for the age of AI.**_ Watch our annual dbt Launch Showcase to hear from our executives and product leaders about the latest features landing in dbt. Watch live demos to see firsthand how these features will empower data developers, data analysts, and organizations in the age of AI. **What you can expect** **Highlights include:** - A **brand new dbt engine** complete with SQL comprehension thanks to our SDF integration - Onboarding more collaborators with a **visual editing experience** and bespoke analyst features - Observing and tuning spend with new **cost management **interfaces and capabilities - ...and much more! ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/9769357081a8582a03786248bb4a6f8bb9c7f798-1800x798.png) ## Who we’re empowering for the next era of analytics **Data developers:** Learn about productivity and developer experience improvements that make it turnkey (and delightful!) to ship high-quality data at speed and scale **Analysts:** See how data analysts can bring their business knowledge to bear and participate in governed, self-serve data development **Organizations:** Optimize every dollar spent on data and standardize on a platform that supports flexible navigation of cross-platform architectures [Watch now](#register) --- --- title: "Accelerating dbt with SDF" description: "SDF Labs joins dbt Labs! Learn how this partnership boosts pipelines, productivity, and analytics." url: "https://www.getdbt.com/resources/webinars/accelerating-dbt-with-sdf" date: "2025-01-14" categories: ["On-Demand"] --- # Accelerating dbt with SDF SDF Labs joins dbt Labs! Learn how this partnership boosts pipelines, productivity, and analytics. [SDF Labs](https://www.sdf.com/) has joined dbt Labs, ushering in the next era of dbt as we know it. SDF’s tech will significantly improve data pipeline velocity, supercharge data developer ergonomics and productivity, and fold new metadata into dbt that will improve data lineage and enable new use cases. Watch dbt Labs founder Tristan Handy and SDF Labs co-founder, Lukas Schulte, as they talk about the significance of bringing these two great technologies together. Be ready to learn about how this new tech will result in delightful improvements to your analytics practice. #### Meet our speakers: - Tristan Handy - Lukas Schulte --- --- title: "Data leaders panel: Building a mature analytics workflow" description: "Data leaders panel on scalable analytical practices featuring special guests from Cox Automotive and Salesforce." url: "https://www.getdbt.com/resources/webinars/data-leaders-panel-building-a-mature-analytics-workflow" date: "2024-12-18" categories: ["On-Demand"] --- # Data leaders panel: Building a mature analytics workflow Data leaders panel on scalable analytical practices featuring special guests from Cox Automotive and Salesforce. Despite technological and process advancements, analytics in practice still suffers from the same problems it did a decade ago: low velocity, inaccurate results, impaired trust…all with increased costs. Progress has been made, but there is more we can do. [In a recent letter](https://www.getdbt.com/resources/guides/the-analytics-development-lifecycle) Tristan Handy, Founder & CEO at dbt Labs, put forward the Analytics Development Lifecycle (ADLC). ADLC is relevant to an entire analytical system, from ingesting data to transforming it to analyzing it to building data products on top of it. He signed off the note with this thought: “What is required now is to collectively roll up our sleeves and push the conversation forward in every single arena.” We pick up the discussion here in a virtual panel moderated by dbt Labs with Tristan, and special guest data leaders from Cox Automotive and Salesforce. **What our customers say about the Analytics Development Lifecycle (ADLC)** “Centralized data teams are often asked to be a catch-all for data related issues. We deal with infrastructure issues, business logic issues, data quality issues, and we…create bottlenecks…My data team can’t scale fast enough to meet the needs of the company. [dbt Cloud] enables us with a distributed model of data management.” - [**JetBlue**](https://www.getdbt.com/case-studies/jetblue) “It was chaos. Now everything's centralized. There's more transparency and much more visibility....dbt has improved our data quality, as well as enabled safe collaboration.” - [**Dish Digital Solutions**](https://www.getdbt.com/case-studies/dish-digital-solutions) “dbt professionalized all transformation steps and enabled us to scale, minimize risk, and increase stability,” - [**Plentific**](https://www.getdbt.com/case-studies/plentific) Our speakers expand on building a consistent, shared analytics framework and discuss: **Key takeaways** - Requirements of a mature analytics workflow including velocity, governance, and resiliency. - How the phases of the ADLC relate to and influence each other. - Collaborating across the three ADLC personas: The engineer, the analyst and the decision maker. - Ways to measure and evaluate workflow maturity in an organization. Watch on-demand as our panelists deliver their tips for scalable analytical practices. **Meet our speakers:** - Brian Lloyd-Newberry - Srini Vemuru - Tristan Handy - Alex Welch --- --- title: "One dbt: Data collaboration built on trust with dbt Explorer" description: "Join us to learn how dbt Cloud connects practitioners and consumers with high-quality data, eliminating silos and building trust." url: "https://www.getdbt.com/resources/webinars/one-dbt-data-collaboration-built-on-trust-with-dbt-explorer" date: "2024-12-11" categories: ["On-Demand"] --- # One dbt: Data collaboration built on trust with dbt Explorer Join us to learn how dbt Cloud connects practitioners and consumers with high-quality data, eliminating silos and building trust. Watch part three of our virtual event series on how dbt delivers a common unified framework for solving complex analytics challenges. This time, we’re focusing on the features and workflows of dbt Cloud that connect practitioners and consumers with high-quality data. Watch now and discover how to explore, improve, and trust your data assets with dbt! **What you’ll gain from watching:** - **Eliminate data silos and build trust:** Learn how dbt Explorer brings practitioners and stakeholders together with high-quality data, making it turnkey for teams to explore, improve, and trust their data assets. - **Understand data impact with auto-exposures:** Discover how auto-exposures offer automatic context into how data products are used downstream, so you can fine tune them and delight your stakeholders. - **Build context with model query history:** See which models are popular (or not) with model query history, helping you prioritize dev work. - **Share real-time signals on data quality:** Embed data health tiles into any downstream application so your stakeholders can build confidence in the data they're about to use. - **Advanced CI for data validation:** Learn how to validate changes to your data products before they’re merged into production, catching any unexpected behaviors before they impact your stakeholders. **Meet our speakers:** - Alexis Jones - Roxi Dahlke --- --- title: "Scaling embedded analytics: Deliver reliable data with the dbt Semantic Layer" description: "Learn how a unified data strategy can elevate your embedded analytics and provide trusted insights to every stakeholder." url: "https://www.getdbt.com/resources/webinars/scaling-embedded-analytics-with-dbt-semantic-layer-virtual-event" date: "2024-10-24" categories: ["On-Demand"] --- # Scaling embedded analytics: Deliver reliable data with the dbt Semantic Layer Learn how a unified data strategy can elevate your embedded analytics and provide trusted insights to every stakeholder. Watch on-demand as our Product Manager, Jordan Stein, alongside Brightside Health’s Chief Data Officer, Hans Nelsen, and Data Engineer, Fidel Ilustre, share how Brightside Health transformed its data strategy using the dbt Semantic Layer. **Key takeaways include:** - Centralize data to improve decision-making with crawlable metrics. - Embed analytics to streamline workflows and reduce manual tasks. - Best practices for leveraging the dbt Semantic Layer to create scalable, real-time insights for anyone. **Learn more** Faced with fragmented metrics, inconsistent data quality, and manual processes, Brightside centralized its metric logic and scaled embedded analytics to support clinical operations and business growth—all while enhancing data governance. In this session, we’ll walk through how Brightside improved operational efficiency, minimized context switching, and scaled analytics for every data consumer, from providers to business users. Learn how a unified data strategy can elevate your embedded analytics and provide trusted insights to every stakeholder. **Meet our speakers: ** - Hans Nelsen - Jordan Stein --- --- title: "One dbt: Accelerate data work with hybrid deployments and cross-platform dbt Mesh" description: "Join us to learn about the new, multi-platform data governance and sharing capability called cross-platform dbt Mesh." url: "https://www.getdbt.com/resources/webinars/one-dbt-accelerate-data-work-with-cross-platform-dbt-mesh" date: "2024-10-22" categories: ["On-Demand"] --- # One dbt: Accelerate data work with hybrid deployments and cross-platform dbt Mesh Join us to learn about the new, multi-platform data governance and sharing capability called cross-platform dbt Mesh. In part two of our One dbt virtual event series, we answer the question, “Does dbt Cloud work with that?”. Watch this on-demand virtual event to learn about new and upcoming features that enable you to manage dbt projects across multiple data platforms. We’ll also show you how we’re making it easier for those hybrid users to keep dbt Core and dbt Cloud in sync. So, save your seat now and we’ll show you how to achieve flexibility and end-to-end visibility with dbt! **What else you’ll hear about:** - **Iceberg right ahead:** Learn how dbt Cloud and the Iceberg table format make cross-platform dbt Mesh a reality. Stay for a demo showing governance, contracts, and lineage in effect across Amazon Athena and Redshift. - **Core or Cloud? Yes!:** No matter your entry point to dbt, we will show you how to use both together to create a mature analytics workflow that delivers business value. **Meet our speakers:** - Amy Chen - Jeff Mills --- --- title: "One dbt: The control plane for data collaboration at scale" description: "Learn how dbt Copilot improves productivity and data quality with auto-generated tests, docs, and semantic models." url: "https://www.getdbt.com/resources/webinars/one-dbt-the-control-plane-for-data-collaboration-at-scale-virtual-event" date: "2024-10-04" categories: ["On-Demand"] --- # One dbt: The control plane for data collaboration at scale Learn how dbt Copilot improves productivity and data quality with auto-generated tests, docs, and semantic models. The recent [Analytics Engineering survey](https://www.getdbt.com/resources/reports/state-of-analytics-engineering-2024) revealed that data quality, ownership, and literacy are among the top challenges facing data teams today. Not addressing these gaps can result in mistrust between the business and the data team and negatively impact company growth. Watch our on-demand virtual event to learn how dbt delivers a common unified framework for solving complex analytics challenges, and see how a data control plane can improve data quality, collaboration, and trust. **We’ll dive into how:** - The new features announced at Coalesce 2024 impact you and your team. - The Analytics Development Lifecycle (ADLC) streamlines workflows. - A data control plane improves flexibility, collaboration, and trust. - AI integrates into the dbt workflow to boost team productivity and data quality. Missed Coalesce 24'? Don’t worry, we’re bringing the highlights to you! Plus, we’ll reveal how you can start applying these features for real business impact today. ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/c07c45614702d041eb7c7cea919c3b58f9ce647c-3095x1777.jpg) **What you can expect** - **What’s New in dbt: **Explore the latest features including the visual editing experience, cross-platform dbt Mesh, and dbt Copilot—built to make your workflow smoother and more collaborative than ever. - **Why It Matters: **Learn how new features in dbt can accelerate many of the stages of the ADLC and why that matters to your business. - **Live Demos: **See how dbt Copilot makes people more productive with auto-generated tests, docs, and semantic models. Check out the natural language chatbot that allows anyone to ask a natural language question like, “What was revenue last quarter?”. **Meet our speakers:** - Alexis Jones - Drew Banin --- --- title: "dbt Run-down: Partner Update Webinar (APJ)" description: "Join us to hear more about the recent product updates announced at our Coalesce event" url: "https://www.getdbt.com/resources/webinars/dbt-run-down-partner-update-webinar-apj" date: "2024-10-03" categories: ["On-Demand"] --- # dbt Run-down: Partner Update Webinar (APJ) Join us to hear more about the recent product updates announced at our Coalesce event ##### About the session Please join us for our inaugural APJ Partner Updates Webinar - “dbt Run-down”. The intent of the session is to keep you informed about all things dbt; this particular session will feature product updates and announcements from Coalesce (our annual analytics engineering conference). **When:** - Wednesday, 30 October 2024 - 11:00AM-12:00PM (JST) - 13:00PM - 14:00PM (AEDT) - 15:00PM - 16:00PM (NZDT) ##### **Agenda:** - Welcome-Introduction - Aaron (5 mins) - Coalesce Updates Recap - Alex (45 mins) - Customer Success story (10 mins) ##### Presenters ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/44e8c289d561398acca7ac3e6011ab4963178988-2212x1086.png) --- --- title: "From complex to simple: Flybuys' move from dbt Core to dbt Cloud" description: "Watch Milos Zikic, at Flybuys, as he reveals their journey moving from self-managed dbt Core to the power of dbt Cloud." url: "https://www.getdbt.com/resources/webinars/from-complex-to-simple-flybuys-move-from-dbt-core-to-dbt-cloud" date: "2024-09-03" categories: ["On-Demand"] --- # From complex to simple: Flybuys' move from dbt Core to dbt Cloud Watch Milos Zikic, at Flybuys, as he reveals their journey moving from self-managed dbt Core to the power of dbt Cloud. Watch our on-demand virtual event where [Milos Zikic](https://www.linkedin.com/in/mzikic/), Lead Enterprise Data Architect at Flybuys, reveals their journey moving from self-managed dbt Core to the power and efficiency of dbt Cloud. [Flybuys](https://experience.flybuys.com.au/), one of Australia’s largest customer loyalty programs with over 9 million members, understands the importance of streamlined, efficient data management. Learn how Flybuys leverages dbt Cloud to: - **Test and Validate New Approaches:** Tie initial marketing use cases to dbt Cloud, assessing the viability of its benefits through strategic pilot programs. - **Streamline Migration:** Execute a phased migration from dbt Core to dbt Cloud, optimizing existing workflows and enhancing overall efficiency. - **Achieve Significant Outcomes: **Learn why the move to dbt Cloud was the right choice for Flybuys and the measurable impact it had on their operations. Discover why Flybuys transitions to dbt Cloud to meet the growing demands of their data infrastructure and how you can apply their strategies to your organization. Watch the on-demand webinar now! **What you can expect** You'll leave the session with: - Practical insights on how others are driving value with dbt Cloud today - A clear view of the migration process from dbt Core to dbt Cloud - A concrete understanding of dbt Cloud's unique product capabilities through a live demo ### Meet our presenters - Mark Wan - Milos Zikic ### Host partner ![Flybuys](https://cdn.sanity.io/images/wl0ndo6t/main/44d92a0f13a665028e61011a2425b9b82339e8db-940x320.png) --- --- title: "Peak data performance: WHOOP’s move from dbt Core to dbt Cloud" description: "Watch Matt Luizzi, at WHOOP, as he reveals their journey moving from self-managed dbt Core to the power of dbt Cloud." url: "https://www.getdbt.com/resources/webinars/peak-data-performance-whoop-s-move-from-dbt-core-to-dbt-cloud" date: "2024-08-30" categories: ["On-Demand"] --- # Peak data performance: WHOOP’s move from dbt Core to dbt Cloud Watch Matt Luizzi, at WHOOP, as he reveals their journey moving from self-managed dbt Core to the power of dbt Cloud. Watch our virtual event where [Matt Luizzi](https://www.linkedin.com/in/matt-luizzi/), Director of Business Analytics, at WHOOP, reveals their journey moving from self-managed dbt Core to the power and efficiency of dbt Cloud. [WHOOP](https://www.whoop.com/us/en/), the wearable fitness tracker trusted by elite athletes like LeBron James, Cristiano Ronaldo, and Rory McIlroy, is a leader in performance tracking. They understand the power of accurate, timely data to drive success. Learn how WHOOP leverages dbt Cloud to: - **Accelerate data delivery: **Launch new commercial products on schedule without sacrificing quality. - **Empower Business Teams**: Utilize a multi-project dbt Mesh architecture and cutting-edge AI to maximize team success. - **Democratize data access**: Improve data quality and visibility for self-service across the organization. - **Cut Costs & Boost ROI:** Minimize manual maintenance, save resources, and and optimize investment returns. Find out why WHOOP transitioned to dbt Cloud to handle their growing data complexity and how you can apply their strategies to your organization. **What you can expect** You'll leave the session with: - Practical insights on how others are driving value with dbt Cloud today - A clear view of the migration process from dbt Core to dbt Cloud - A concrete understanding of dbt Cloud's unique product capabilities ### Meet our presenters - Sara Gawlinski - Matt Luizzi ### Host partner ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/5db3cd7db15accd7c1fe42ee46fbda149f917dbd-1760x288.png) --- --- title: "Fireside chat: A Data Team’s University Life" description: "Join data experts from RMIT and University of Canterbury for an engaging fireside chat" url: "https://www.getdbt.com/resources/webinars/fireside-chat-a-data-teams-university-life" date: "2024-06-23" categories: ["On-Demand"] --- # Fireside chat: A Data Team’s University Life Join data experts from RMIT and University of Canterbury for an engaging fireside chat Ever wondered about the world of data within a university setting? Join us for an engaging fireside chat where we dive deep into the topic with the experts from RMIT University in Melbourne, Australia, and the University of Canterbury in Christchurch, New Zealand. In this session, we will explore a variety of topics, from the challenges and triumphs of migrating off legacy platforms to envisioning the future landscape of data within the academic sphere. Learn how these institutions handle vast amounts of information, ensure data integrity, and leverage data for innovation and research. Don't miss this opportunity to gain insights from leading data professionals who are at the forefront of transforming the educational data environment. ### **Meet our speakers** ![Image](https://cdn.sanity.io/images/wl0ndo6t/main/0bba5a6721a755afc86d59730f4c254e586db4f7-1060x338.png) --- --- title: "dbt Labs on dbt: Streamlining KPI Dashboards with the dbt Semantic Layer" description: "Learn how dbt Labs uses dbt Cloud and the dbt Semantic Layer to automate real-time, accurate KPI dashboards across BI tools." url: "https://www.getdbt.com/resources/webinars/dbt-labs-on-dbt-streamlining-kpi-dashboards-with-the-dbt-semantic-layer" date: "2024-06-11" categories: ["On-Demand"] --- # dbt Labs on dbt: Streamlining KPI Dashboards with the dbt Semantic Layer Learn how dbt Labs uses dbt Cloud and the dbt Semantic Layer to automate real-time, accurate KPI dashboards across BI tools. _Like any good software vendor, we’re REALLY BIG FANS of our own platform here at dbt Labs. We’re hosting an entire “dbt Labs on dbt” series to showcase how various teams at our company take advantage of dbt Cloud to build and take advantage of trusted data._ ✨ Watch our event on demand where we'll dive into how dbt Labs leverages dbt Cloud—and specifically the dbt Semantic Layer—to automate our business KPI dashboards with real-time, accurate metrics across various BI tools. Find out how we transformed a previously manual, hectic, and error-prone process into a streamlined, efficient workflow. **What to expect** - **Automation and Accuracy:** Learn how our Data team uses the dbt Semantic Layer to simplify dashboard creation, significantly reducing manual tasks and minimizing errors. - **Best Practices:** Discover naming conventions and other best practices that can optimize your own data workflows. - **Transformative Insights:** See compelling "before and after" statistics that highlight the impact of these improvements. - **Future Strategies:** Hear about our forward-looking plans for enhancing analytics workflows, including faster projections, easier data exploration, and AI integrations. **Meet our presenters:** - Alex Welch - Paige Berry --- --- title: "Boost AI Reliability with dbt Cloud" description: "Learn how dbt Cloud can enhance your AI initiatives with reliable and accurate data, reduce AI hallucinations, accelerate data-driven projects, and lower the barrier to analytics." url: "https://www.getdbt.com/resources/webinars/boost-ai-reliability-with-dbt-cloud" date: "2024-06-01" categories: ["On-Demand"] --- # Boost AI Reliability with dbt Cloud Learn how dbt Cloud can enhance your AI initiatives with reliable and accurate data, reduce AI hallucinations, accelerate data-driven projects, and lower the barrier to analytics. Learn how dbt Labs pulls back the curtain to demystify AI and LLMs for data teams. Gain a richer understanding of what you can do to deliver AI initiatives powered by trustworthy and accurate data — _today_ — with the help of dbt Cloud in this on-demand webinar. ** What you can expect:** - **Practical, actionable tips** to equip you for success as you build AI-powered data experiences. - **A deep dive** into how dbt Cloud users can solve real-world problems today using Generative AI. - **dbt Assist: **Check out the new AI-enabled workflow (now in beta) that quickly generates documentation and tests in dbt Cloud, helping you boost productivity and enhance data quality. - **In-depth demos:** to show the end-to-end workflow in action and bring it all home. Check out Ask dbt, a new feature of our Snowflake Native App powered by the dbt Semantic Layer and Snowflake Cortex. **Meet our speakers:** - Drew Banin - Luis Leon - Jason Ganz - Azzam Aijazi --- --- title: "2024 dbt Cloud Launch Showcase" description: "Watch our executive & product leaders reveal the latest dbt Cloud innovations designed to help you deliver Data That Works." url: "https://www.getdbt.com/resources/webinars/dbt-cloud-launch-showcase" date: "2024-04-05" categories: ["On-Demand"] --- # 2024 dbt Cloud Launch Showcase Watch our executive & product leaders reveal the latest dbt Cloud innovations designed to help you deliver Data That Works. _Data is just data. The magic is in _what you do_ with that data to convert it from raw inputs into actionable strategic insights; to transform it from “just data” into Data That Works _✨ Watch dbt Labs exclusive virtual launch event, where executive and product leaders reveal the latest dbt Cloud innovations designed to help you deliver Data That Works. You’ll witness detailed demos, learn about our newest releases, and get a peek into what’s coming to dbt Cloud over the next few months. [Watch video](https://getdbt.wistia.com/medias/ujvcmd7tfq) ## Hear from executive leadership Get an inside look into long-term platform vision and how we're delivering on it with these new releases. [Watch now](#register) ## See detailed demos from product experts Dive deep into our newest platform features with live demos and expert talks to help you start getting value today. [Watch now](#register) ## How we're delivering Data That Works **Control and quality:** Keep those pipelines humming and people happy with new ways to build transformations, continuously integrate analytics code, automate testing, and proactively troubleshoot issues. **Connections**: Get a holistic view of your estate and trace and orchestrate your entire workflow from source to metric to consumer. **Collaboration:** Make it easy and intuitive for downstream stakeholders to understand and use data in their day-to-day. [Watch now](#register) --- --- title: "2024 State of Analytics Engineering Webinar" description: "Watch experts give their perspectives on industry benchmarks, macro trends,& strategies for building effective data organizations." url: "https://www.getdbt.com/resources/webinars/2024-state-of-analytics-engineering-webinar" date: "2024-03-23" categories: ["On-Demand"] --- # 2024 State of Analytics Engineering Webinar Watch experts give their perspectives on industry benchmarks, macro trends,& strategies for building effective data organizations. The results are in. [The 2024 State of Analytics Engineering report](https://www.getdbt.com/resources/reports/state-of-analytics-engineering-2024) is out now — revealing the experiences of data practitioners and leaders in dbt Labs's annual survey. Watch this on-demand webinar with experts from the analytics engineering space, Jason, Lauren, and Ian, as they discuss industry benchmarks, macro trends, and strategies for building effective data organizations. [Watch video](https://getdbt.wistia.com/medias/5t7k5112ol) ## Explore the evolving landscape of data analytics - Learn how both large and small data teams are adjusting to changes in analytics tools. - Understand the impact of macro-environmental shifts on data teams and their leaders. - Discover essential information about Generative AI and its relationship with data teams. - Q&A: Share your thoughts, reactions, and questions. We're all ears! [Watch Now](#register) ## Meet your presenters In this session, you’ll have a chance to chat face-to-face with experienced analytics engineering specialists, creating a lively space for sharing knowledge and ideas. The conversation will be guided by the topics that interest both the experts and you, making it a friendly and collaborative learning experience. - [Jason Ganz](https://www.linkedin.com/in/jasnonaz/) Manager, Developer Experience, dbt Labs - [Lauren Benezra](https://www.linkedin.com/in/lbenezra/) Senior Analytics Engineer, dbt Labs - [Ian Macomber](https://www.linkedin.com/in/ian-macomber/) Head of Analytics Engineering and Data Science, Ramp [Watch Now](#register) --- --- title: "New in dbt Cloud: A four-part series on maximizing value" description: "Explore the latest dbt Cloud advancements in our on-demand webinar series. Dive into new features over four sessions to elevate your team's data strategy." url: "https://www.getdbt.com/resources/webinars/new-in-dbt-cloud-a-four-part-series-on-maximizing-value" date: "2024-03-20" categories: ["On-Demand"] --- # New in dbt Cloud: A four-part series on maximizing value Explore the latest dbt Cloud advancements in our on-demand webinar series. Dive into new features over four sessions to elevate your team's data strategy. Welcome to what's New in dbt Cloud, an on-demand webinar experience designed to guide you through the latest advancements in dbt Cloud. This will help your team unlock new levels of efficiency and insight. Over four comprehensive sessions, we'll dive deep into each new feature and show you how to leverage them to supercharge your business's data strategy. Whether you're new to dbt Cloud or seeking to deepen your understanding, this series offers something for everyone. Tune into today and start maximizing the value of dbt Cloud for you and your team. [Watch video](https://getdbt.wistia.com/medias/ajsjui0hba) **Series Highlights:** **Episode 1: How to Get Value Out of Everything New in dbt Cloud** Dive into dbt Cloud's latest innovations, including the new Cloud CLI, CI features, dbt Explorer, dbt Semantic Layer, and dbt Mesh. Discover why these features are not just exciting but crucial for your business's growth and success. **Episode 2: How to Get Value from dbt Mesh** Explore dbt Mesh in action and learn how to manage data complexity at scale effectively. This session will demonstrate how to empower distributed data teams, enhance model governance, and accelerate insights. **Episode 3: How to Get Value from the dbt Semantic Layer** Unlock the full potential of the dbt Semantic Layer and revolutionize how your team shapes and analyzes data. Gain insights into defining metrics centrally, ensuring data reliability, and streamlining processes. **Episode 4: How to Get Value from dbt Explorer** Get acquainted with dbt Explorer and the opportunities it brings for data discovery. Understand how to visualize, navigate, and manage your dbt projects more effectively, improving pipeline performance and reducing costs. --- --- title: "Build for scale, agility, and reliability with dbt Cloud, AutomateDV, and Data Vault" description: "Move away from inconsistent and siloed data models to a unified, scalable architecture that supports agile development and simplifies data complexity." url: "https://www.getdbt.com/resources/webinars/build-for-scale-agility-and-reliability-with-dbt-cloud-automatedv-and-data-vault" date: "2024-02-27" categories: ["On-Demand"] --- # Build for scale, agility, and reliability with dbt Cloud, AutomateDV, and Data Vault Move away from inconsistent and siloed data models to a unified, scalable architecture that supports agile development and simplifies data complexity. #### **About the session:** Move away from inconsistent and siloed data models to a unified, scalable architecture that supports agile development and simplifies data complexity. Watch now and discover how to leverage the power of dbt Cloud and AutomateDV to build a streamlined, dependable, and scalable data architecture. You’ll learn strategies to implement template-driven development, automatic documentation, and integrated testing, enhancing your team’s productivity. Additionally, you’ll gain an understanding of what Data Vault is and why enterprises like McDonald’s Nordics are using it. #### What you can expect: **Live Demonstrations:** Learn directly from the specialists at dbt Labs and Datavault who are redefining data management. Witness a comprehensive walkthrough of how to build a Data Vault with dbt Cloud and AutomateDV. **Practical Solutions:** Explore how to overcome your data modeling challenges, such as lack of version control, testing difficulties, and unsustainable bloat. **Exclusive Announcements:** Be the first to hear about our new shared commercial offerings designed to help you get started quickly. ##### Hosted by: ![Partners](https://cdn.sanity.io/images/wl0ndo6t/main/299e09d9eb067c1088808acddc1ebe8e1f6f7c8e-3478x400.png) ### Meet our speakers In this session, you’ll have a chance to chat with experienced dbt and Data Vault experts making it a friendly and collaborative learning experience. You’ll see a live, detailed demo on building an end-to-end Data Vault with dbt Cloud and AutomateDV. --- --- title: "The time is now: Migrating from stored procedures to dbt Cloud" description: "Switch to dbt Cloud for collaborative, version-controlled data modeling. Learn migration patterns and benefits from our experts." url: "https://www.getdbt.com/resources/webinars/migrating-from-stored-procedures-to-dbt-cloud" date: "2024-02-20" categories: ["On-Demand"] --- # The time is now: Migrating from stored procedures to dbt Cloud Switch to dbt Cloud for collaborative, version-controlled data modeling. Learn migration patterns and benefits from our experts. Are you tired of managing your SQL scripts, maintaining complex stored procedures, and spending countless hours debugging? If so, it’s time to switch to dbt—the modern data modeling tool that makes SQL and data transformation collaborative, modular, and version-controlled. In this session, we’ll look at an example of moving a stored procedure to dbt, talk about patterns to help you think about migration, and look at some of the benefits of moving to dbt including lineage, less boilerplate, modularity and simplified debugging and deployment. Watch and learn how dbt can modernize your data modeling process. Our experts will walk you through the benefits of using dbt and demonstrate how it can save you time and reduce errors in your workflow. #### Meet our presenters [Lee Bond-Kennedy](https://www.linkedin.com/in/leebondkennedy/), Senior Solutions Architect at dbt Labs (Host) [Matt Winkler](https://www.linkedin.com/in/matt-winkler-4024263a/), Solutions Architect, Team Lead at dbt Labs (Host) [Ernesto Ongaro](https://www.linkedin.com/in/eongaro/), Solutions Architect, Team Lead at dbt Labs (Host) --- --- title: "Optimizing costs in your data workflow" description: "Hear from experts on AI, self-service, and SaaS consolidation strategies." url: "https://www.getdbt.com/resources/webinars/optimizing-costs-in-your-data-workflow" date: "2024-02-20" categories: ["On-Demand"] --- # Optimizing costs in your data workflow Hear from experts on AI, self-service, and SaaS consolidation strategies. Data teams have always been asked to do more with less. “Can you make this easier? Can this run faster?” That focus on efficiency hasn’t changed. But what has changed is the macroeconomic pressure to tighten budgets, consolidate tools, and find cost optimizations wherever possible in the data development process. As teams look back on the lessons of 2023, and look ahead at the budgets of 2024, this webinar provides a candid discussion—and tangible tips—for how to make the most of your resources. #### What you’ll learn In this webinar panel, data practitioners who optimized resources in their data workflow share exactly: - What they did - The impacts the initiative had - What they learned - The advice they have for other teams Plus, the panelists share their perspectives on some of the most talked-about ideas to drive efficiency with spend: AI, self-service, modernization, and SaaS consolidation. #### Meet the speakers - **[Clay Townsend](https://www.linkedin.com/in/clay-townsend-5a549286/),** Principal Data Architect, Sharp HealthCare, Panelist - [**Pratik Vij**](https://www.linkedin.com/in/pratikvij/), Senior Manager, Data Engineering, Total Wine, Panelist - [**Mike Moyer**](https://www.linkedin.com/in/mikemoyer1/), Data Engineer, Paxos, Panelist - [**Elize Papineau**](https://www.linkedin.com/in/epapineau/), Senior Data Engineer, dbt Labs, Panelist - **[Steve Dowling](https://www.linkedin.com/in/stephen-dowling-jr/),** Senior Solutions Architect, dbt Labs, Presenter - **[Mallory Busch](https://www.linkedin.com/in/yeamal/),** Senior Product Marketing Manager, dbt Labs, Host --- --- title: "Less maintenance, More ROI: Why Code42 moved from dbt Core to dbt Cloud" description: "Learn how Code42 cut 40+ hours of weekly maintenance and improved dashboard uptime by migrating from dbt Core to dbt." url: "https://www.getdbt.com/resources/webinars/less-maintenance-more-roi" date: "2024-02-20" categories: ["On-Demand"] --- # Less maintenance, More ROI: Why Code42 moved from dbt Core to dbt Cloud Learn how Code42 cut 40+ hours of weekly maintenance and improved dashboard uptime by migrating from dbt Core to dbt. ##### About the session: As your company scales, so does the complexity of managing and maintaining its data infrastructure. Don’t let easily-avoidable maintenance work hold your data team back from delivering value to the business. Watch now and hear firsthand how Code42 migrated from self-managed dbt Core to dbt Cloud on Snowflake and enhanced their data operations to: - Scale their dbt deployment without compromising data quality - Save 40+ hours a week on maintenance - Improve dashboard uptime from 80% to 95+% Code42’s Director of Analytics, Josh Carlson, watch this session to dive into Code42’s journey of moving to dbt Cloud, adopting a robust CI/CD process to improve data quality, and accelerating ROI. Gain actionable insights and proven strategies to get the most out of your dbt deployment and set your team up for scale. Watch now! --- --- title: "How to get value from the dbt Semantic Layer" description: "Discover how to leverage the dbt Semantic Layer for consistent data insights and improved analytics in this on-demand webinar." url: "https://www.getdbt.com/resources/webinars/how-to-get-value-from-the-dbt-semantic-layer" date: "2024-02-20" categories: ["On-Demand"] --- # How to get value from the dbt Semantic Layer Discover how to leverage the dbt Semantic Layer for consistent data insights and improved analytics in this on-demand webinar. Elevate your data decisions in 2024 with the dbt Semantic Layer, now generally available! Dive into this on-demand webinar where you’ll explore the powerful capabilities that will transform the way you and your teams shape and analyze data. With the dbt Semantic Layer, data teams have a scalable approach to defining metrics centrally and downstream data consumers have the confidence that they’re making decisions from trustworthy data. Say goodbye to data discrepancies, and hello to streamlined processes. This is episode 3 of our 4-part series on how to get value from everything new in dbt Cloud. You’ll walk away empowered to get your organization to move faster with a foundation of reliable, unified data. #### What you can expect **Expert-led Demos:** Learn how to define metrics in dbt Cloud and enable downstream users to seamlessly query those definitions across a variety of analytics tools. **Business Boosters:** Understand how the dbt Semantic Layer reduces complexity, aligns business and data teams on consistent definitions, and optimizes governance and productivity. ## Meet the speakers In this session, you’ll have a chance to chat with experienced dbt experts making it a friendly and collaborative learning experience. You’ll see a live deep dive demo into the dbt Semantic Layer and learn how to get started with dbt Cloud today. ## On-demand: How to get value from dbt Mesh Did you miss the second installment of this series where our product specialists showcased the innovations in dbt Mesh and various access points including the all-new dbt Explorer and Cloud CLI? Tune into this on-demand session to learn how to navigate and manage data complexity at scale effectively. [Watch the on-demand webinar here](https://www.getdbt.com/resources/how-to-get-value-from-dbt-mesh). --- --- title: "How to get value from dbt Explorer" description: "Explore how dbt Explorer enhances data discovery and project navigation in this webinar, ideal for scaling dbt Cloud projects." url: "https://www.getdbt.com/resources/webinars/how-to-get-value-from-dbt-explorer" date: "2024-02-20" categories: ["On-Demand"] --- # How to get value from dbt Explorer Explore how dbt Explorer enhances data discovery and project navigation in this webinar, ideal for scaling dbt Cloud projects. Enhance your work process with dbt Explorer – a fresh knowledge base and visualization tool in dbt Cloud. Join us for this live session where you’ll dive into the endless possibilities that dbt Explorer brings to the world of data discovery. Leveraging the metadata created from each dbt Cloud run, dbt Explorer empowers both data developers and data consumers to navigate the intricate web of data resources. This on-demand session is episode 4 of our 4-part series on how to get value from everything new in dbt Cloud. You’ll walk away empowered to help your team better understand, navigate, and improve your global dbt projects. #### What you can expect **Expert-led Demos:** Our product specialists will showcase dbt Explorer’s newest capabilities—including column-level lineage—and how your team can better visualize, understand, and manage their dbt projects at scale. **Business Boosters:** Learn how data developers and data consumers alike can use dbt Explorer to understand resources and their interdependencies, quickly troubleshoot and resolve issues, and proactively improve pipeline performance and costs**.** ## Meet the speakers In this session, you’ll have a chance to chat with experienced dbt experts making it a friendly and collaborative learning experience. You’ll see a live deep dive demo into dbt Explorer and learn how to get started with dbt Cloud today. *Explore more from dbt Labs* ## On-demand: How to get value from the dbt Semantic Layer Did you miss the third installment of this series where our product specialists showcased the innovations in the dbt Semantic Layer? Tune into this on-demand session to learn how to reduce complexity, align business and data teams on consistent definitions, and optimize governance and productivity. [Watch the on-demand webinar here](https://www.getdbt.com/resources/how-to-get-value-from-the-dbt-semantic-layer). --- --- title: "Business critical data teams: Taming the chaos of data democracy" description: "Watch our on-demand webinar to see how dbt Cloud empowers teams to access, understand, and leverage data for better decisions." url: "https://www.getdbt.com/resources/webinars/data-democracy" date: "2024-02-20" categories: ["On-Demand"] --- # Business critical data teams: Taming the chaos of data democracy Watch our on-demand webinar to see how dbt Cloud empowers teams to access, understand, and leverage data for better decisions. Data democracy sounds great in theory. Reality is more complex. As much as every business would like to get critical data into the hands of stakeholders, there’s significant risk that comes from exposing pipelines and models to a larger group of people. The solution isn’t to shut down data access entirely and create bottlenecks to decision-making. Instead, businesses should take a governed approach to data access. By implementing measures—technical and cultural—that set limitations around access and make debugging easier, businesses can strike the right balance between promoting data democracy and avoiding data anarchy. #### What you'll learn In this webinar, panelists will share their perspective on the wild, wild west of data democracy, as well as how to embed governance throughout the data workflow. Attendees will come away with tangible tips on how to implement and up-level governance initiatives within their organizations, from both a technical and cultural perspective. #### Panelists - [**Natalie Greenwood**](https://www.linkedin.com/in/nataliegreenwood/), Head of Data Governance at [Analytics8](https://www.analytics8.com/) - [**Michael Colella**](https://www.linkedin.com/in/michaelnicholascolella/), Senior Director, Global Data & Strategy Analytics at [AXS](https://www.axs.com/) - [**Liz Connors**](https://www.linkedin.com/in/lizhouck/), Analytics Lead at [Mission Lane](https://www.missionlane.com/) - [**Zola Petkovic**](https://www.linkedin.com/in/zola-petkovic-33167410/), Sales Director, dbt Labs --- --- title: "How to get value from dbt Mesh" description: "Dive into dbt Mesh and empower distributed teams. Learn to manage data complexity at scale and accelerate insights." url: "https://www.getdbt.com/resources/webinars/how-to-get-value-from-dbt-mesh" date: "2024-02-14" categories: ["On-Demand"] --- # How to get value from dbt Mesh Dive into dbt Mesh and empower distributed teams. Learn to manage data complexity at scale and accelerate insights. Ready to see dbt Mesh in action? Watch a deep dive into how to navigate and manage data complexity at scale effectively. Finding business insights in your data gets more difficult as data projects grow in complexity. That’s where dbt Mesh, now available in dbt Cloud, comes in. This is the second episode of our 4-part series on how to get value from everything new in dbt Cloud. We'll show you how you can empower distributed data teams to work together more effectively by breaking apart the project monolith. Tune into this on-demand session! #### What you can expect **Expert-led Demos:** See product specialists showcase the innovations in dbt Mesh and various access points including the all-new dbt Explorer and Cloud CLI. **Business Boosters:** Understand how features like cross-project refs, model governance, and interactive lineage are essential for scaling your data projects and accelerating insights. --- --- title: "When and how to adopt dbt Cloud" description: "Explore adopting dbt Cloud with insights from dbt experts. Learn about building a case, monitoring, and solving data quality issues. Ideal for dbt users and data team managers." url: "https://www.getdbt.com/resources/webinars/when-and-how-to-adopt-dbt-cloud" date: "2024-02-14" categories: ["On-Demand"] --- # When and how to adopt dbt Cloud Explore adopting dbt Cloud with insights from dbt experts. Learn about building a case, monitoring, and solving data quality issues. Ideal for dbt users and data team managers. So you’re finally getting the hang of testing with dbt—that’s great! But how do you go from the occasional ❌ in your tests to having an actual plan to keep your pipelines running smoothly? Check out dbt Labs as we explore when and how to adopt dbt Cloud with the simplicity and power of the Snowflake Data Cloud. Each session shows the why and how behind scaling dbt capabilities to production readiness. Watch the recording of this first session now, as we discuss how to operationalize data testing with [Randy Pitcher](https://www.linkedin.com/in/randypitcherii/), Sr Solutions Architect at dbt Labs. #### What you’ll hear - How organizations have built the case for dbt Cloud - How to decide when it is time to add ongoing monitoring and alerting with dbt Cloud - The actual business problems that are caused by having low visibility and immature operations when data breaks - How dbt tests solve part of this problem with accessible data quality features for the busy data engineer or time-constrained analyst #### Who this is for - Users of open source dbt who are interested in understanding the benefit and value of dbt Cloud. - Managers of data teams either using or considering using dbt and are curious to learn more about data quality and observability. How does it work? Is this worth doing? Why not just keep doing what you’re doing? - Executive decision-makers under pressure to deliver more data products with stricter budgets. Can you afford a pipeline failure the night before the highest revenue day of your year? What updates do you have for your stakeholders about how you are making sure your teams are delivering high-quality outcomes and spending less at the same time? We hope you’ll join us for an outcomes-focused session about scaling data quality at your organization with no magic🪄, black boxes, or fairytales—just proven practices your competitors are already implementing today. --- --- title: "Why and how to adopt dbt Cloud" description: "Learn about adopting dbt and maintaining documentation. Discover strategies, real case studies, and best practices. Ideal for dbt users, data professionals, managers, and decision-makers." url: "https://www.getdbt.com/resources/webinars/why-and-how-to-adopt-dbt-data-documentation" date: "2024-02-14" categories: ["On-Demand"] --- # Why and how to adopt dbt Cloud Learn about adopting dbt and maintaining documentation. Discover strategies, real case studies, and best practices. Ideal for dbt users, data professionals, managers, and decision-makers. Welcome to round ✌️ of why and how to adopt dbt. Ready to learn how to maintain healthy documentation over time with dbt? Great! Once you’ve become comfortable with testing using dbt, it’s important to have a plan in place to ensure that your models are documented so your business logic is captured and shareable. Join dbt Labs as we delve into the topic of adopting dbt Cloud and explore the best practices behind scaling dbt capabilities to production-readiness. Watch this recording to discover effective strategies, gain insights from successful implementations, and ensure that your data documentation remains robust, reliable, and valuable to your organization. Don’t miss out on this opportunity to elevate your data documentation practices with dbt. #### What you’ll hear - The business implications of having inadequate or outdated data documentation - How you can run documentation coverage checks before moving model changes to production - Strategies for determining when is the right time to require documentation - Real-life case studies of organizations that have successfully transformed their data documentation using dbt Cloud #### Who this is for - Users of open source dbt who are interested in understanding the benefit and value of dbt Cloud - Data engineers, analysts, and data professionals responsible for maintaining data documentation - Managers and team leads interested in optimizing data quality and documentation practices - Decision-makers looking to enhance data accessibility and reliability while optimizing resources #### Presenters [Lauren Benezra](https://www.linkedin.com/in/lbenezra/), Senior Analytics Engineer at dbt Labs [Victoria Perez Mola](https://www.linkedin.com/in/victoriaperezmola/), Solutions Architect at dbt Labs --- --- title: "The ROI of dbt Cloud" description: "Discover how dbt Cloud delivers a 194% ROI by improving data trust, collaboration, and self-service analytics." url: "https://www.getdbt.com/resources/webinars/study-forrester-tei-webinar" date: "2024-02-14" categories: ["On-Demand"] --- # The ROI of dbt Cloud Discover how dbt Cloud delivers a 194% ROI by improving data trust, collaboration, and self-service analytics. Looking to understand how you can save costs and improve trust in your data? Watch now to learn about how dbt Cloud can help. The webinar features a guest from Forrester speaking to the study findings. dbt Labs recently commissioned Forrester Consulting to conduct a Total Economic Impact™ study on the benefits of deploying dbt Cloud. It examined the potential return on investment enterprises gain by investing in a more governed, accessible, and scalable approach to data transformations. We cover how moving beyond outdated stored procedures or drag-and-drop tools can help you: - Accelerate development to deliver trusted data quickly - Standardize processes to improve collaboration - Securely increase self-service data analytics to foster more innovation --- --- title: "Business critical data teams: Building a demand prediction engine at TIER" description: "Learn from TIER Mobility's data-driven success story. Discover how they utilized dbt Cloud to fuel exponential growth and optimize demand prediction." url: "https://www.getdbt.com/resources/webinars/tier-mobility" date: "2024-02-14" categories: ["On-Demand"] --- # Business critical data teams: Building a demand prediction engine at TIER Learn from TIER Mobility's data-driven success story. Discover how they utilized dbt Cloud to fuel exponential growth and optimize demand prediction. ![aws snowflake logos](https://cdn.sanity.io/images/wl0ndo6t/main/b490fa0e46112aa5bea5eeb2424b7e579b325d38-500x77.svg) How does a micro mobility startup rise to capture some of the world’s largest markets as the leading provider? They invest in data-driven workflows & operations. Check out this conversation with Jerry Nwabuilo, Senior Analytics Engineer and Kumar Aman, Engineering Lead at TIER. During this fireside chat, Kumar and Jerrry will share their insights and experiences with TIER’s cutting edge approach to a data-first culture. #### About TIER [TIER Mobility](https://www.tier.app/en/) is the world’s leading shared micro-mobility provider, with a mission to c_hange mobility for good._ By providing people with a range of shared, light electric vehicles—from e-scooters to e-bikes—TIER helps cities reduce their dependence on cars. Founded in 2018, TIER currently operates in 560+ cities across 31 countries, including London, Paris, Berlin, San Francisco, and Dubai. #### Meet the presenters Jerry Nwabuilo, Senior Analytics Engineer at TIER Kumar Aman, Engineering Lead at TIER Sofia Sulikowski, Product Marketing Manager at dbt Labs What can you expect? - TIER’s journey to 10x data team members and 500x rides and how dbt Cloud supported the exponential growth **_alongside Snowflake and AWS_** - How TIER built a demand prediction model to ensure their vehicle placement & parking incentives allowed them to maximize revenue - Q&A with Kumar and Jerry around TIER’s resulting new business opportunities --- --- title: "dbt 101: A real-time look into dbt Cloud" description: "Explore dbt Cloud in this 60-minute on-demand webinar with use cases, live demos, and tips for adding dbt to your data stack." url: "https://www.getdbt.com/resources/webinars/dbt-101-live-june-session" date: "2024-02-14" categories: ["On-Demand"] --- # dbt 101: A real-time look into dbt Cloud Explore dbt Cloud in this 60-minute on-demand webinar with use cases, live demos, and tips for adding dbt to your data stack. It’s easy to get started in dbt Cloud—if you know what it does. But if you’re like thousands of data practitioners that are still working out how dbt fits into the modern data stack, and why it matters, dbt 101 can help. Watch a 60-minute on-demand presentation. We’ll start with some of our most popular use cases, show dbt Cloud in action, and hear from real dbt users on how they navigated everything from stakeholder alignment to team training, and measuring success. We promise you’ll leave knowing how to explain dbt to anyone, with the confidence to launch your own project. #### What you can expect - Top dbt use cases - A live look at the dbt Cloud platform - Where dbt fits in your stack, and who can use it #### Meet our presenters [Randy Pitcher](https://www.linkedin.com/in/randypitcherii/), Solutions Architect at dbt Labs [Gwen Windflower](https://www.linkedin.com/in/gwenwindflower/), Senior Developer Experience Advocate --- --- title: "Business critical data teams: How data fuels growth at ClickUp" description: "Business critical data teams: How data fuels growth at ClickUp" url: "https://www.getdbt.com/resources/webinars/business-critical-data-teams-how-data-fuels-growth-at-clickup" date: "2024-02-14" categories: ["On-Demand"] --- # Business critical data teams: How data fuels growth at ClickUp Business critical data teams: How data fuels growth at ClickUp Ready to take your business’s growth to the next level with advanced analytics? Well, dbt Cloud can help you do just that! Watch our fireside chat with [Marc Stone](https://www.linkedin.com/in/marcstone/), Head of Data and Analytics at ClickUp, and [Clarke Patterson](https://www.linkedin.com/in/clarkepatterson/), VP of Product Marketing at dbt Labs, **watch now** and learn how ClickUp is using dbt to influence how their data teams work more closely with business systems to create new avenues for business use-cases. During this session, you’ll discover how dbt can boost your data team’s morale by helping them meet and exceed demands from internal stakeholders. Plus, you’ll learn how better data modeling can support growth marketing and sales funnels, giving you a competitive advantage in the market. #### About ClickUp ClickUp is a cloud-based collaboration and project management tool that is perfect for businesses of any size and industry. Some features include task assignments, statuses, alerts, and a customizable task toolbar. #### What you’ll learn - How to leverage data to optimize and personalize the customer experience - The role of data modeling in scaling and accelerating business growth - Best practices for aligning your business and data teams to drive growth - Real-life case studies of businesses that have grown through effective data management and analytics --- --- title: "Business critical data teams: Becoming indispensable to the CPO at Nasdaq" description: "Learn how Nasdaq approaches self-service data in this on-demand fireside chat with dbt Labs and Nasdaq's Lead Product Manager." url: "https://www.getdbt.com/resources/webinars/nasdaq-fireside-chat" date: "2024-02-14" categories: ["On-Demand"] --- # Business critical data teams: Becoming indispensable to the CPO at Nasdaq Learn how Nasdaq approaches self-service data in this on-demand fireside chat with dbt Labs and Nasdaq's Lead Product Manager. Do you have questions about how data and product teams should collaborate? If so, watch this on-demand conversation with Brian Taylor, Lead Product Manager at Nasdaq. During this fireside chat, Brian will share his insights and experiences with building a self-service data platform with the billions of messages in Nasdaq’s data lakehouse—so business product managers can access market data now and when they need it. Hear from one of the leading voices in the financial industry and have your questions answered in a relaxed and casual setting—don’t miss out! Who is Nasdaq: Nasdaq is the second largest stock exchange in the world, managing 30 exchanges across North America and the Nordics, and home to over 4,000 public companies. They also provide technology to 2,200 financial institutions in 130 markets. #### Meet our presenters [Brian Taylor](https://www.linkedin.com/in/brian-taylor-85762b4b/), Lead Product Manager at Nasdaq [Sofia Sulikowski](https://www.linkedin.com/in/sofia-sulikowski/), Customer PMM at dbt Labs #### What you can expect - An overview of Nasdaq’s self-service data product, built on the modern data stack - How the data team partners with the product org to enable strategic product insights - Q&A with Brian around Nasdaq’s resulting new business opportunities --- --- title: "2023 State of Analytics Engineering Webinar" description: "The State of Analytics Engineering Webinar" url: "https://www.getdbt.com/resources/webinars/state-of-analytics-engineering-webinar" date: "2024-02-13" categories: ["On-Demand"] --- # 2023 State of Analytics Engineering Webinar The State of Analytics Engineering Webinar #### About the session Results are in for [**The State of Analytics Engineering Report**](https://www.getdbt.com/state-of-analytics-engineering-2023/) — the pains and gains shared by data practitioners and leaders, in dbt Labs’s first-annual survey of this fast-changing space. Check out Jason, Erica, and Emily as they discuss industry benchmarks, macro trends shaping the industry, and strategies for creating effective data organizations 🔥 #### What you can expect - Key trends and forecasts: what analytics engineering looks like across the globe - Spotlight on practitioners: how they spend time, where they find friction, and where they look to invest in 2023 - Spotlight on leaders: how people managers navigate today’s challenges, and prime their teams for success #### Meet our presenters - [Jason Ganz](https://www.linkedin.com/in/jason-ganz-a5839052/), Manager of Developer Experience, dbt Labs - [Erica Louie](https://www.linkedin.com/in/ericalouie/), Head of Data, dbt Labs - [Emily Hawkins](https://www.linkedin.com/in/hawkinsemily/), Data Engineering Manager, GlossGenius --- --- title: "How to get value out of everything new in dbt Cloud" description: "Unlock dbt Cloud's latest features: explore innovations, assess business impact, gain expert insights in this on-demand session." url: "https://www.getdbt.com/resources/webinars/how-to-get-value-out-of-everything-new-in-dbt-cloud" date: "2024-02-13" categories: ["On-Demand"] --- # How to get value out of everything new in dbt Cloud Unlock dbt Cloud's latest features: explore innovations, assess business impact, gain expert insights in this on-demand session. Explore dbt Cloud's latest product launches and features. If you saw dbt Cloud in action at Coalesce, we're going to go deeper into why it matters to your business, and how you can start applying these new features to drive business value today. If you missed Coalesce, we're here to share the magic with you. Now it's your turn to discover how these innovations can be applied to your day-to-day and supercharge your team's capabilities. Tune into this on-demand session! #### What you can expect - **What’s new in dbt:** We'll walk through innovations in the new Cloud CLI and CI features, dbt Explorer, dbt Semantic Layer, and dbt Mesh. - **Business Boosters:** Understand why these new features aren't just exciting but are essential for the growth and success of your business. - **Expert Guidance**: Learn about these game-changing features from dbt experts. ## Explore dbt Cloud - **Advanced developer experience:** dbt Cloud introduces CLI, offering flexibility in IDE and terminal choice. - **Streamlined docs and discovery:** Navigate dbt Cloud projects effortlessly with highly performant dbt Explorer. - **Universal metrics:** Define and access metrics centrally in dbt, ensuring consistent, widespread insights through dbt Semantic Layer. - **Empower domain teams:** Own dbt projects with dbt Mesh, fostering rapid development and collaboration while upholding data governance. [Watch now](#form) ## Meet our speakers In this session, you’ll have a chance to chat face-to-face with experienced dbt experts making it a friendly and collaborative learning experience. You’ll walk away getting a live look into new dbt Cloud features and how to get started with it today. [Watch now](#form)