Data Lineage

Data lineage records how data moves and changes from an origin to a destination. It connects source datasets, processing activities, outputs, and responsible systems or people. The result is a graph that can answer two practical questions: where did this data come from, and what depends on it?

The central mental model is a map plus evidence.

source dataset -> transformation job -> curated table -> metric -> dashboard
      node              node                node          node       node
          \____________ directed dependency edges ________________/

The map shows connected assets. The evidence describes the transformations, executions, times, versions, and owners behind those connections. A diagram without reliable evidence becomes stale. A stream of evidence without stable identities cannot form a useful graph.

Why data lineage exists

Modern data crosses databases, files, streams, transformation tools, schedulers, and reporting systems. A field on a dashboard may have passed through many jobs before a reader sees it. Local documentation rarely explains the entire path.

Lineage makes that path queryable. You can use it to:

trace a questionable result toward its upstream inputs;
identify downstream assets before changing a table or field;
discover which job produced a dataset;
connect an execution failure to affected outputs;
find owners and documentation attached to a path;
preserve evidence about how a result was produced.

Lineage does not prove that data is accurate. It records relationships and context. A fully traced calculation can still contain an incorrect rule.

Data Lineage

Data Lineage

Why data lineage exists

A graph of entities and activities

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