What is Data Lineage

Definition

Data lineage is the documented map of data's journey through a system, tracing each dataset back to its origins and forward to its uses so teams can see exactly how a value was produced, what transformations it passed through, and what depends on it.

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Pinpoints the upstream source of any value so errors can be traced to their origin fast
Reveals downstream impact before a change, preventing accidental breakage of reports and models
Supports audits and compliance by proving how regulated figures were derived
Speeds root-cause analysis when a dashboard shows an unexpected number

Glossary: AI Data Governance

Real World Example

When a finance dashboard shows a suspicious revenue spike, an analyst uses lineage to trace the figure back through three transformation jobs to a duplicated source feed, fixing the cause in minutes instead of days.

FAQs

Why is data lineage important?

It lets teams trace any value to its source and see what depends on it, which is essential for debugging, audits, and safe change management.

How is lineage captured?

Modern catalogs and pipeline tools capture lineage automatically by parsing transformation code and tracking data movement between systems.

What is the difference between lineage and provenance?

Lineage maps the flow and transformations of data through systems, while provenance focuses on the origin and authenticity of the data itself.