BigQuery
Connect every BigQuery table and view to the sources that feed it and the models, metrics, and dashboards that consume it, regardless of vendor.
Warehouses & LakehousesWhy BigQuery matters
BigQuery holds the data, but it cannot tell you where a table came from or which dashboards go dark when a view is altered. The real stack around it is rarely all-Google: teams pair BigQuery with dbt, Fivetran, Tableau, Salesforce syncs, and dozens of other tools that have no visibility into each other.
Typedef connects BigQuery to the rest of your stack, giving you column-level lineage from any upstream source through transformation to any downstream consumer, so every change is scoped and every incident has a clear trail.
What Typedef unlocks
Full-stack impact analysis
Before changing a table, see exactly what breaks downstream across every system that depends on it, not just within BigQuery.
Cost and usage attribution
Connect warehouse spend to the actual dashboards, teams, and business use cases driving it, so cost conversations are grounded in ownership.
Metric provenance
Trace conflicting numbers back to their source definitions across the full platform, whether the discrepancy originates in BigQuery, the semantic layer, or the BI tool.
Migration and cleanup confidence
Plan migrations to or from BigQuery with full visibility into every upstream source and downstream dependency. Deprecate unused models knowing nothing still depends on them.
Related integrations
See the full picture: what feeds every table and what depends on it across the stack.
Trace dependencies across notebooks, Delta tables, Unity Catalog, and every tool outside Databricks that depends on them.
Map every table, view, and stored procedure to its full upstream and downstream footprint across the stack.
Go beyond the DAG: trace every model through the full platform, not just the project.