Databricks
Connect Delta tables, notebooks, jobs, and Unity Catalog objects to the upstream sources and downstream consumers across the full platform.
Warehouses & LakehousesWhy Databricks matters
Databricks blurs the line between data engineering, analytics, and ML. Notebooks, Delta tables, Unity Catalog, and jobs all create dependencies across multiple surfaces. But Unity Catalog only sees what lives inside Databricks. Teams pair it with dbt, Fivetran, Airflow, external BI tools, and CRMs that Unity Catalog cannot reach.
Typedef extends visibility beyond Databricks into every system it connects to, so you can scope changes, attribute costs, and trace issues across the full platform, not just the lakehouse.
What Typedef unlocks
Full-stack impact analysis
Before changing a Delta table, notebook, or job, see exactly which downstream dashboards, metrics, and pipelines are affected, including those outside Databricks.
Cost and compute attribution
Tie cluster and job costs back to the business use cases and teams that drive them, so spend optimization is based on actual downstream value.
Metric provenance
Trace conflicting numbers across the full platform, whether definitions originate in Unity Catalog, dbt, a semantic layer, or a BI tool.
Migration and cleanup confidence
Retire notebooks, consolidate redundant tables, or plan migrations with full visibility into every dependency across the stack.
Related integrations
See the full picture: what feeds every table and what depends on it across the stack.
Map every dataset, table, and scheduled query to its upstream sources and downstream consumers across the full stack.
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.