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Data Catalog & Lineage
Making data discoverable and traceable — what a dataset means, where it came from, and what depends on it.
What it is
A data catalog makes datasets discoverable and documented; lineage tracks where a piece of data came from and what downstream depends on it — together, they answer "what is this, and what breaks if I change it."
Key points
- Catalog: searchable metadata (descriptions, owners, schema, sample values) for every dataset — without one, "what data do we even have" becomes tribal knowledge locked in a few people's heads.
- Lineage: traces a field or table back through every transformation to its source, and forward to everything that consumes it — critical for impact analysis before changing a shared table's schema.
- Automated vs. manual lineage: modern tools parse SQL/pipeline code to build lineage automatically; manually maintained lineage documentation reliably goes stale.
- The practical payoff: when a metric looks wrong, lineage is what lets you trace backward to the actual source of the problem instead of guessing.