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