Knowing what feeds what
As pipelines multiply, no one can hold the whole graph in their head. Data lineage records how each table and column is derived from upstream sources, and a data catalog makes datasets discoverable with descriptions, owners, and schemas.
What lineage answers
- Impact analysis asks if I change this column, what breaks downstream. Lineage shows every dependent table and dashboard.
- Root cause asks why is this report wrong, tracing back through transformations to the bad source.
- Trust and discovery lets analysts find the authoritative table and see who owns it instead of guessing.
How it is built
Lineage is often extracted automatically by parsing query and pipeline definitions to see which inputs produce which outputs. A catalog layers searchable metadata, classifications, and ownership on top, so people find and understand data without reading code.
Key idea
Lineage maps how data is derived end to end for impact analysis and root cause, while a catalog adds searchable metadata and ownership so people discover and trust the right datasets.