Data documentation

Collaborate from a foundation of shared understanding.

Why documentation matters

Understanding how metrics like "ARR" are defined, and what source data they depend on, should not require a ticket or a DM. dbt docs helps everyone in your organization, including business users, answer everyday questions about datasets.

A foundation for collaboration

Data documentation in dbt

dbt allows you to generate a fresh docs site alongside each run of your data transformations.:

Define key fields

Dig into the field + table descriptions annotated by your collaborators.

Examine data lineage

Trace the provenance of a column or table back through your project's dependency graph.

Explore source freshness

Investigate whether raw source data is fresh or stale.

View analytic code inline

Pop into the data model itself for ad-hoc investigation while browsing docs.

Take action from your docs

dbt docs allow you to codify your organization's critical communication workflows.

Source freshness

Set alerts for stale data

Out of the box, dbt supports schema tests for uniqueness, null or accepted values, or referential integrity between tables.

These can be extended by writing your own custom schema tests.

Set alerts for stale data


Track downstream applications

Exposures allow contributors to trace the status of the upstream dbt models that their reports & analysis workflows depend on.

Track downstream applications

Data Lineage

Develop with your colleagues in mind

dbt automatically infers your dependency graph as you code, eliminating the drain of manual dependency tracing.

This allows you to proactively work with any stakeholders who may be affected by your modeling updates.

Develop with your colleagues in mind</h3>

Document data where it lives

Whether your analytics data is stored in a cloud warehouse, data lake, lake house or beach house - running dbt docs will propagate table & column definitions to your schema.