Debugging data issues in a distributed environment requires tracing a value back to its source. Without a clear map of dependencies, root cause analysis effectively relies on manual inspection of codebases and logs. Data lineage provides this map by capturing the relationships between datasets, pipelines, and dashboards. It shifts metadata from being a passive reference into an active component of the reliability stack.

This section examines the technical implementation of lineage tracking. We begin by differentiating between static analysis, which parses SQL or Python code to identify references, and dynamic analysis, which captures lineage based on runtime execution. We then review the OpenLineage standard to understand how to format and transmit metadata across different tools in the stack.

You will also learn to model dependencies mathematically. By treating the data platform as a graph $G = (V, E)$, where $V$ represents data assets and $E$ represents the transformation jobs, we can algorithmically determine the scope of a failure. We apply this concept to impact analysis, allowing engineers to predict which downstream reports will break if an upstream schema changes. The chapter concludes with a hands-on exercise involving the extraction of lineage edges from application logs to reconstruct a pipeline view.