data-lineage

You can track data lineage in Python notebooks by wrapping DataFrame pipelines with the tracked_pipe wrapper, which records transformations and row-count changes to create an auditable record of data evolution.

You can visualize DataFrame transformation lineage by exporting Mermaid diagrams that map pipeline flows, providing a quick graphical review for auditing and documentation of data evolution.

Yes, data lineage tracking applies to standard Python notebooks and scripts during data wrangling to produce an auditable record of transformations for reproducibility.

You need tracked_pipe to wrap DataFrame pipelines because it actively monitors and records row-count changes across transformations, ensuring transparent auditing of how data evolves.

Yes, the Skill exposes a lineage management API that allows you to programmatically manage data transformation tracking across notebooks and pipeline workflows.

The best way to document data evolution across notebooks is to wrap pipelines with tracked_pipe to record transformations and export Mermaid visualizations for transparent auditing.