Data Lineage

Data lineage records every layer of transformation from raw data sources to final reports or decisions, and version management ensures that data tables, feature definitions, and model artifacts can all be traced back to a specific point in time.

The concept of data lineage originates from the data warehouse and data governance field, with typical implementations including ETL pipeline DAGs, run ids, input/output hashes and version snapshots.

A lineage-first pipeline requires each synthetic sample to come with a manifest containing the generating model checkpoint hash, prompt template version, sampling parameters, filter identity and thresholds, seed source, and batch acceptance/rejection rates.

Data lineage refers to tracing the complete path of each data metric from its original source to its final presentation, including which systems it passes through, which transformations, and which cache; in AI agent scenarios, three questions must be answered: which system authoritatively produces the number, how much latency or caching it goes through, and who is responsible for its correctness.

This design is commonly referred to in the data engineering field as data lineage or extraction provenance

Data lineage can trace the full chain of data from its source to final consumption, helping to quickly identify the scope of impact when upstream tables undergo changes or quality issues.

Unity Catalog's lineage tracking achieves visibility at the column level (Column-level Lineage).

When enterprises adopt AI-generated reporting tools, establishing manual review mechanisms and data lineage tracking are necessary supporting measures to guard against the risk of misleading outputs.

Data lineage tracking records the complete flow path of data from its source to consumption, helping to understand how metrics are calculated, the upstream data sources they depend on, and business logic transformations.