What is Data Lineage

How It Works

Data lineage systems track the flow of data through pipelines by recording provenance metadata at each processing step. For each data artifact, lineage records its source, the transformations applied, the code and configurations used, and the downstream artifacts it produced. This creates a directed acyclic graph (DAG) that can be traversed to understand how any output was derived from raw inputs.

Technical Details

Lineage is captured at different granularities: table-level (which tables feed which), column-level (field-level dependencies), and row-level (individual record tracking). Implementation approaches include explicit instrumentation in pipeline code, automatic extraction from query logs, and metadata APIs. Standards like OpenLineage provide a common format for lineage events. Storage uses graph databases or lineage-specific metadata stores.

Best Practices

• Record lineage automatically at each pipeline stage rather than relying on manual documentation
• Track lineage at the document level in multimodal systems to link processed outputs to source files
• Include processing parameters and model versions in lineage metadata for full reproducibility
• Build lineage visualization tools so teams can explore data flow intuitively

Common Pitfalls

• Implementing lineage tracking as an afterthought, making retrofit costly and incomplete
• Recording only table-level lineage when column or row-level detail is needed for debugging
• Not connecting lineage across system boundaries (e.g., between ETL and ML training)
• Storing lineage data without retention policies, leading to unbounded metadata growth

Advanced Tips

• Use lineage to implement automated impact analysis when source data changes
• Build lineage-powered debugging that traces a bad output back through every transformation step
• Implement lineage-based compliance reporting for data governance regulations
• Track multimodal lineage chains from source files through decomposition, embedding, and indexing