Blog archive: 2026
- Making the economic case for warehouse-first AI context
- A letter from our founders
- How organizations progress from trusted data to trusted AI and agents
- How to assess your organization’s AI readiness
- How Ramp's internal AI agents crossed a million sessions
- How Obie Insurance cut its month-end close from 25 hours to 2 with dbt State and a lake house rebuild
- How Virgin Media O2 cut compute costs 30% by giving dbt a memory
- What Okta learned governing context for AI agents at scale
- How to put generative AI directly into a dbt pipeline with plain SQL
- How Sigma runs a 2,883-model semantic layer on dbt and Dagster
- ClickHouse + dbt: Building a GitOps workflow at Twilio
- How KOHO built a Claude-powered self-serve analytics tool on years of dbt governance
- Why an AI agent's revenue query was $3.5 million wrong, and how AWS fixed it with dbt Semantic Layer
- How DoorDash scaled merchant-facing analytics to thousands of users in weeks, on dbt and ThoughtSpot
- Why most of your dbt models aren't in a hurry, and what that's worth in Snowflake credits
- How Ecolab used an AI migration agent to shrink 84,000 lines of legacy SQL by 6x
- How Checkr cut an hours-long metrics investigation to minutes by engineering AI context on dbt and Omni
- How Guitar Center rebuilt its data platform on trust
- How Sweetwater catches broken dbt pipelines before anyone notices, with Datadog
- How LangChain grew self-serve analytics questions 40x by giving its AI agent layered context
- How a one-person data team at Keep built real-time banking infrastructure on dbt
- Ship AI agents like tables: dbt materializations for Snowflake CoWork
- Charts worth stealing: the design calls BI should ship
- dbt Charts: a new open-source language for dashboards that live inside your dbt repo
- What a year of building ADBC drivers taught Columnar about zero-copy's non-zero problems
- Why a semantic layer is the difference between a 96% and an 80% accurate AI analyst
- With great context comes great autonomy
- dbt without the warehouse (or the bill): DuckDB end to end
- Apache Iceberg on Snowflake got a lot simpler in 2026, and dbt's new catalog config is what makes it click
- Why dbt Labs thinks AI agents will finally make the semantic layer stick
- How dbt Labs and Fivetran are moving dbt workloads onto the data lake, model by model
- Inside dbt v2: how a rebuilt SQL engine makes linting up to 250x faster
- What Open Data Infrastructure looks like with dbt Mesh
- How EF Education First built a cost and value layer for dbt models with a Snowflake Cortex Agent
- How KOHO built a Claude-powered self-serve analytics tool on years of dbt governance
- How Data Culture measured its way from zero trust to a shippable AI analytics agent
- How Verily Health monitors wastewater for 30-plus pathogens with a 5-person team and 1 dbt package
- How Kramp cut analyst onboarding from days to minutes with a self-serve dbt Mesh factory
- How customer pressure pushed dbt past a cautious platform team at Delivery Hero
- Beyond medallion architecture: the written rules that keep a growing dbt project consistent
- Inside dbt Wizard, the AI agent built to show its work
- dbt v2 lands: faster parsing, one shared adapter layer, and a docs experience that skips the giant manifest.json
- Optimizing your runs for lower compute, fresher data, and faster iteration with dbt State
- Showing our work: a controlled experiment in context engineering
- From prompt to PR: how Careem’s AI writes and validates dbt models before a human ever sees them
- How DocuSign cut analytics engineering time in half by pairing Claude Code with dbt v2, dbt MCP, and agent skills
- From mainframe to AI-powered maturity: Verisk's data modernization journey
- What a swarm of rogue AI agents can teach us about having better ideas
- How to solve dashboard debugging in six minutes with an MCP-connected AI agent
- How a data scientist at Kilo built the business context layer his AI agents were missing
- The dbt governance framework that cut new project setup time 70%
- A dbt "logic mesh" with packages: standard model and metrics across 25 autonomous organizations
- How Figma turned dbt tests into consistency checkers that catch 100+ bugs a month
- How Mitratech cut new customer data onboarding from six weeks to three days with dbt
- How Compare the Market cut report delivery from 6 weeks to under 1 by retiring most of its dashboards
- Shrink the surface: governed self-service for analyst-owned dbt pipelines
- Meta:Context: a business context schema in dbt's semantic layer
- How WMCHealth cut a 72-minute dbt pipeline down to 5 minutes, with one environment variable
- How WebstaurantStore used Snowflake Cortex AI to automate dbt staging for 700+ sources in 90 days
- How Recruit Holdings, the company behind Indeed, taught its text-to-SQL agent to read a website's hidden context
- How PetScreening turned a dbt pipeline into a pet tag factory
- How AlphaSense turned a 3-to-5-week data rebuild into a few hours with dbt and Euno's AI context
- When skills meet scale: an agent assembly line for dbt
- Why context engineering belongs in the data warehouse
- dbt Champions: What we learned from founding members
- dbt State now available free forever
- dbt now supports Amazon Redshift data sharing — and your pipelines just got a lot faster
- With dbt State, stop rebuilding what didn't change
- Context engineering is already possible in your warehouse. Here's how to get started.
- Fivetran + dbt Labs Announces New Capabilities to Make Enterprise Data Agent-Ready at dbt Summit 2026
- Everything we announced at dbt Summit and why it matters
- We built dbt State to stop rebuilding what hadn't changed
- Celebrating the 2026 dbt partner of the year winners
- Why your AI pilot stalled at the context gap
- Scaling AI is easy. Trusting it is hard.
- Databricks processes your data. dbt defines what it means
- dbt Core v1.12 is GA
- Model for the token, not the table
- Why agentic projects fail and how to fix them
- How dbt State cuts warehouse compute and speeds up every run
- dbt Summit 2026: the keynotes and product sessions
- From analytics engineer to context engineer
- Retiring the dbt Snowflake Native App
- Fivetran + dbt Labs: The future of dbt Core v2.0
- Your next level starts here: A preview of dbt Summit sessions, by role
- OSI is now Apache Ossie (Incubating)
- The productivity gains hiding in your data infrastructure
- Solving dashboard errors in minutes: How Integral Ad Science used MCP to connect agents to dbt and Databricks
- A guide to implementing AI data pipelines
- Data platforms were built to store. Intelligence platforms are built to reason.
- What Fivetran + dbt Labs brought to Databricks Data + AI Summit (and what you can take home)
- The semantic debt crisis no one is talking about
- Start fresh, don't lift and shift: a dbt migration guide
- The analytics engineer in 2026: system designer, governance owner, AI context provider
- How dbt makes agentic data pipelines trustworthy: the transformation layer's role in autonomous data systems
- Context engineering is the new analytics engineering skill: a practical guide for dbt users
- Building the agentic data stack: A practical dbt guide for the AI era
- The trust-speed paradox: Governing AI-accelerated data work
- Your AI isn't broken. Your data model is.
- What is enterprise data infrastructure?
- Building a data stack for trusted AI
- dbt Labs Named Snowflake Data Integration Product Partner of the Year
- What we announced at Snowflake Summit and why it matters
- Fivetran and dbt are one company now. Here's what that means.
- Fivetran + dbt Labs Complete Merger to Create the Data Infrastructure for Trusted AI Agents
- What data infrastructure do agents need?
- Are you ready for the dbt Fusion engine?
- Get dbt certified. Stay certified. Stay ahead.
- AI-ready data in practice: What dbt Semantic Layer and dbt's MCP server and agent skills do for your team
- What's shipped in dbt — May 2026
- AI-assisted analytics engineering: Docusign’s framework for scaling dbt unit testing
- How Nasdaq built a governed intelligence layer with dbt and Databricks
- Ship smarter agents in production with dbt Agent Skills
- The dbt Developer Agent is now in Preview: the coding agent for analytics engineering
- 5 dbt MCP server patterns that work in production
- How Obie cut compute costs by 30%, reclaimed engineering hours, and built stronger governance
- Using dbt with Databricks: Architecture decisions that determine success
- dbt Labs Wins a 2026 Google Cloud Partner of the Year Award
- Why metric definitions matter for reliable AI agents
- Meet Antigravity: Google’s agentic IDE enters the dbt orbit
- Exploring dbt and Google with AI agents
- Tableau and dbt: structured context for reliable AI analytics
- New dbt Labs Report Finds AI-driven Acceleration is Outpacing Trust and Governance
- From raw data to trusted AI: What dbt is bringing to Google Cloud Next
- Mistakes I made as the head of analytics (and what I’d do differently now)
- Operationalize analytics agents: dbt AI updates + Mammoth’s AE agent in action
- How AI is reshaping the way data practitioners work
- What does Databricks Lakebase mean for analytics engineers?
- Introducing the dbt Community Champions Program
- Types of data transformations for machine learning
- What are the most common data pipeline architecture patterns?
- How a semantic layer prevents AI hallucinations in analytics
- How ETL tools fit into modern data pipeline architecture
- The Iceberg ecosystem today
- Why metadata management is critical for modern data teams
- Why ETL is still essential for modern data pipelines
- How a global investment firm reduced runtimes by 30–40% with the dbt engine
- Effective strategies to enhance data quality management
- Data movement patterns explained (ETL, ELT, CDC & more)
- How data transformation improves data quality and analysis
- Effective strategies to improve data quality across your organization
- How AI improves data lineage at scale
- How AI is transforming modern data pipelines
- Write once, analyze anywhere: Omni + the dbt Semantic Layer
- How Zscaler cut PR review time by 90% using dbt context and multi-agent AI (OpenAI)
- Data ins and outs for 2026: what data teams are keeping, cutting, and reconsidering
- How AI accelerates and improves data modeling
- How dbt Labs reduced dbt-related compute costs by 64% with Fusion and state-aware orchestration
- Maximize the business value of your data platform with dbt
- Bring structured context to Snowflake Intelligence with dbt
- Do you need a data observability platform?
- dbt Core v1.11 is GA
- Deliver reliable AI with the dbt Semantic Layer and dbt MCP Server
- How data mesh solved centralized data management challenges
- Why analytics engineering isn't just data modeling
- What defines a control plan for data infrastructure
- Data marts vs. Data products: What's the difference?
- What the Open Semantic Interchange (OSI) spec means for metrics, semantics, and AI
- What makes a great dbt Summit presentation? (And why yours belongs on stage)
- Centrally defined metrics: The key to AI success
- Apache Iceberg and the catalog layer
- How Sweetgreen transformed unstructured data into conversational analytics with dbt and AI
- The role of the semantic layer in data governance and security
- Introducing ADE-bench: measuring how AI agents perform data work
- 29%+ warehouse savings: How the dbt Fusion engine drives cost efficiency
- Closing the context gap: Cribl’s blueprint for trusted AI with dbt + Omni
- Incorporating version control into data analytics
- Data transformation vs. Data modeling: Key differences
- Finding your people in data (and building a community that actually sticks)
- AI agents and the data lake
- Understanding LSP: What it is, and what you can use it for