Data lineage is an essential process that traces the relationships among datasets within an organization's data ecosystem, offering a comprehensive view of data origins, transformations, and destinations. This visibility is crucial for troubleshooting, decision-making, and ensuring compliance, as it allows for a clear understanding of data flow and dependencies, which is vital in today's complex and distributed data environments. Data lineage complements data classification by categorizing data based on sensitivity and importance, enhancing security and compliance efforts. Tools like OpenLineage and Marquez provide standardized frameworks for capturing and querying lineage metadata, enabling organizations to manage data lifecycle, troubleshoot pipeline issues, and maintain data integrity more effectively. Unlike task hierarchy views in tools like Airflow, which show task dependencies, data lineage maps dataset-to-dataset relationships, making it easier to identify and resolve issues that span multiple systems or teams. Companies like Astronomer integrate Airflow and OpenLineage to provide enhanced visibility into the data ecosystem, allowing for faster resolution of data outages, better understanding of cross-team dependencies, and improved data quality and performance monitoring.