DataWorkersProject

Walk a data incident from symptom to root cause: correlate the anomaly, trace the failing upstream job, and produce a root-cause narrative with the evidence chain.

Run a full data-quality audit on a table or pipeline: quality score, rule checks, anomalies with context, and concrete fixes ranked by impact.

Trace end-to-end lineage for any table or column across catalogs. Answers 'where does this data come from and what breaks if it changes' with a source-to-consumer chain.