Operational Health: Auditing data freshness with dlt metadata

Aman Gupta, a Data Engineer, discusses the importance of auditing data freshness using dlt metadata, highlighting that a "Success" exit code merely indicates that a pipeline ran, not that the data it processed is up-to-date. By using a mock lemonade stand as a data source, Gupta illustrates how a freshness check can be built by joining _dlt_loads with the source table and comparing timestamps. The process involves examining when the pipeline last ran and determining if the data is stale by contrasting the source's native timestamp with the dlt's inserted_at timestamp. This approach reveals that pipeline status and data freshness are distinct metrics, emphasizing the need to analyze both to ensure operational health and data accuracy.