Visualize data lineage with dbt Explorer

The Analytics Development Lifecycle

Less untangling pipelines. More delivering value.

dbt Catalog helps your team visualize dependencies, trace data lineage and optimize performance throughout the Analytics Development Lifecycle (ADLC). Learn how lineage tracking, usage insights, and performance monitoring help teams understand and improve their dbt projects—faster.

Read the ADLC whitepaper

Why dbt Catalog

Boost your data insights with a self-generating knowledge base.

dbt Catalog automatically creates metadata, documentation, and lineage for your data, allowing teams to easily explore the status and relationships of their data platform assets in a user-friendly interface.

See all your context in one place

Quickly find and view dependencies and metadata details of your team's data assets without leaving dbt

Reuse—don't rebuild

Increase productivity by letting developers easily discover and reuse existing assets

Troubleshoot faster

Save debugging time by tracing table- and column-level lineage to quickly spot and resolve issues

Get the context you need to navigate and improve your data products.

Align teams and optimize pipelines with a unified understanding.

End-to-end lineage

Automatically build and visualize your end-to-end lineage graph—from data source all the way to the dashboard or AI endpoint your models power—with detailed metadata into each node

Build better with dbt today.

dbt is how modern data teams ship and scale trusted data.

Book a demo Explore dbt

dbt Explorer makes it easy for our consumers to understand the entire lineage from the source to reporting – and all of the data quality checks or issues along the way – without having to “go ask a dev”. The performance feature saved my team hours (if not days) analyzing model build durations over time so we could make intelligent decisions about job orchestration and scheduling.
Robert Goodman Lead Developer of Enterprise Analytics @Lennar
Column-level lineage in dbt Explorer streamlines root-cause and impact analysis. Rather than painstakingly tracing a column forward or backward in our lineage graph, we’re now able to easily follow it up and downstream. As a result, we’ll be able to troubleshoot issues more quickly, and develop a more accurate understanding of potential data model changes.
Katie Claiborne Staff Analytics Engineer @Cityblock Health
With auto-exposures in dbt Explorer, it’s like going from a treasure hunt to having a treasure map. The native Tableau integration and auto-generated lineage help us see everything clearly, making impact analysis across hundreds of dashboards a breeze!
Rahavan Raman Director - Data Engineering & Analytics @Zscaler
Leveraging the model query history feature in dbt Cloud has transformed our approach to optimization. It empowers us to gain deep insights into our SQL execution, identify performance bottlenecks, and enhance our data models with confidence. We're excited to see how this feature evolves in the roadmap ahead.
Gary How Data & Analytics Architect @Kenvue
dbt Explorer has been instrumental in elevating our data modeling and analytics processes. We gained valuable insights into project data quality and adherence to dbt best practices. It not only helped us pinpoint areas for code enhancement but also significantly improved our documentation practices. We achieved substantial enhancements in data quality percentages, effectively mitigating data errors in the bronze/silver layer and ensuring a higher standard of data quality for our end consumers. dbt Explorer is an indispensable ally for any data-driven organization aiming for excellence in their analytics workflows.
Shravan Banda Solutions Architect @World Bank