dbt source freshness: configure checks and handle stale data
Configure dbt source freshness, choose warning and error thresholds, inspect results, and decide what downstream jobs should do when data is late.
Copyable approval workflows about data-pipelines.
Configure dbt source freshness, choose warning and error thresholds, inspect results, and decide what downstream jobs should do when data is late.
Compare source and target data with a runnable SQL example. Find missing rows and mismatched values, investigate differences, and review exceptions.
Define a data contract with schema, meaning, quality rules, and ownership. Separate automated validation from review of breaking changes.
Plan an Airflow backfill, choose reprocessing behavior, limit concurrency, and review historical reruns before they change downstream data.
Use dbt full refresh for incremental models, check configuration overrides, preview model selection, and add approval before a production rebuild.