What is Data Lineage?

Data lineage maps the flow, transformation, and dependencies of data from source systems through processing stages to dashboards, reports, models, and other consumption points, helping organizations trace unexpected results, assess change impacts, and meet compliance requirements. It is particularly important for regulated reporting, data quality investigations, machine learning governance, and operational resilience, but comprehensive implementation is difficult across heterogeneous platforms because of inconsistent metadata, schema changes, column-level transformation complexity, identity mapping issues, retention limits, and incomplete query-log reconstruction. The recommended approach is to begin with high-value use cases and table-level lineage, adopt interoperable standards such as OpenLineage, collect raw lineage events for iterative modeling in a lakehouse, and expand to granular column-level coverage where risks are greatest. Starburst is presented as a platform that can emit OpenLineage events, stream query activity through Kafka, federate lineage data with operational metadata, enforce access controls, and support near-real-time analysis, while emphasizing that lineage freshness, performance, governance, and careful temporal modeling are essential to maintaining reliable results.

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