Manage Data Complexity at Scale with dbt Cloud
Businesses need help to keep up with the expanding data landscape. Technical debt plagues organizations of all sizes. Inefficient code, poor documentation, and monolithic projects eat up time. Data teams are stretched between maintaining their sprawling data estate and producing meaningful results.
Data work shouldn’t require an act of heroism. With the right tools, data teams can be more agile while providing consistent, quality data products that get teams out of constant reactive mode while mitigating costs and technical debt.
dbt Labs is focused on building a platform that solves the data complexity problem at scale. Let’s take a look at what makes that problem so hard.
Why data management has become so complex
Three factors drive the challenges of managing data at scale:
- Unreliable data assets
- Fragmented processes
- Unpredictable costs
Unreliable data assets
Many organizations deal with untrustworthy data that sows distrust in strategic decision making. With data coming from many sources and various teams taking custom approaches to transform and define that data, different departments end up with different analysis results. It doesn’t matter how much data you have if teams can’t agree on whether the data is accurate.
Unreliable data begins with fragmented processes. The complexities of large-scale data management and the bottlenecks inherent in workflows lead to stakeholders seeking out ad-hoc solutions to their data requests. Inevitably, data assets are duplicated, metrics are inconsistent, and pipelines remain fragile without proper testing or documentation. Without proper governance, achieving a truly data-driven approach to decision making remains out of reach and cross-team collaboration suffers, as each team is busy holding its own system together.
With a disorganized process, inefficiencies are everywhere. This leads to growing and unpredictable cloud computing spend, and lost engineering time spent fixing issues in the code base. Unexpected costs—in terms of resources, budget, and productivity—can drag down the whole organization.
How dbt Cloud helps
dbt Cloud provides the standardized approach and tools you need to:
- Build trust in data and data teams
- Ship data products faster
- Reduce the cost of producing insights
Building trust in data
dbt Cloud is built to scale so your teams can focus on proactive collaboration, not maintenance. A federated but accessible structure means everyone in your org—regardless of their technical skillset—can participate in the data workflow, while maintaining proper governance posture. Since dbt Cloud is intuitive to administer, data teams can focus on delivering results rather than reacting to pipeline issues and one-off data requests.
Ship data products faster
dbt Cloud offers an accessible development experience, so anyone with SQL or Python experience can write optimized, reusable code. Git-enabled version control and continuous integration make data teams more productive and code more hardened. Visual interfaces and integrations with analytics tools empower users to improve pipelines and self-serve data assets. This amounts to increased organizational agility as organizations prioritize data-driven initiatives. as.
Reduce the cost of producing insights
dbt Cloud helps organizations optimize their data investments. The visibility that dbt provides allows teams to target inefficiencies in their data pipelines, save developer time, and optimize compute costs. A 2023 study conducted by Forrester Consulting and commissioned by dbt Labs found that a composite organization that invests in dbt Cloud over three years saw a 194% return on investment.
dbt Cloud can help your organization manage the complexity of large-scale data management.
Last modified on: Jan 17, 2024