Redshift
Connect Redshift tables, views, and stored procedures to the sources that feed them and every model, dashboard, and consumer downstream.
Warehouses & LakehousesWhy Redshift matters
Redshift clusters often carry years of accumulated tables, undocumented views, legacy stored procedures, and fragile ETL patterns. Many teams are mid-migration to Snowflake, Databricks, or BigQuery, which makes understanding what depends on what even more critical. The real stack around Redshift spans far beyond AWS.
Typedef maps every Redshift object to its full upstream and downstream footprint across the platform, so you can scope migrations, clean up technical debt, and trace issues without guesswork.
What Typedef unlocks
Full-stack impact analysis
Before modifying a table, view, or stored procedure, see every downstream dependency across BI tools, semantic layers, and consumers, not just within AWS.
Cost and performance attribution
Connect compute and storage costs to the actual queries, teams, and business use cases driving them.
Metric provenance
Trace conflicting numbers back to their source across the full platform, whether the issue is a stale materialized view, a mismatched join, or a divergent BI filter.
Migration and debt cleanup
Map the full blast radius of a Redshift migration across every system. Safely deprecate legacy tables and retire stored procedures with confidence.
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.
Trace dependencies across notebooks, Delta tables, Unity Catalog, and every tool outside Databricks that depends on them.
Go beyond the DAG: trace every model through the full platform, not just the project.