What is data lineage, and how is it captured in Fabric?

• A. Data lineage is the process of archiving datasets for compliance.
• B. Data lineage tracks data origin and movement within source systems only.
• C. Data lineage tracks data origin, movement, and transformations across pipelines and lakehouse; Fabric captures lineage via pipeline definitions, dataflows, and table metadata.
• D. Data lineage is only stored in notebooks.

Answer: (C)

Data lineage is the end-to-end visibility of where data comes from, how it moves through systems, and how it is transformed along the way, all the way from source to its final destination in the lakehouse. In Fabric, this lineage is captured by tying together the facts from pipeline definitions, dataflows, and the metadata of tables. That means you can trace a piece of data from its source, through each transformation or step in a pipeline, to where it resides in a table, with the transformations clearly reflected in the lineage graph. This end-to-end view supports impact analysis, governance, and auditing, since you can see not just where data originated but every point it passed through and how it was changed.

The other ideas don’t fit because archiving datasets is about storing data for retention, not tracking its origin and movement. Limiting lineage to the source systems misses the downstream steps and transformations that occur after ingestion. And storing lineage only in notebooks isn’t how Fabric organizes or surfaces end-to-end provenance; lineage is captured in pipeline definitions, dataflows, and table metadata.