From Data Lineage to Data Observability

Building Systems That Understand Their Own Health

In our previous article, Understanding Data Lineage, we established a critical distinction. Your data lineage map reveals the path your data travels, yet it remains silent on its condition. It’s a schematic confirming a job ran, while leaving the trustworthiness of its output unknown.

Consider the green checkmark next to your Spark job: a classic sign of monitoring. It indicates that a task is finished, but provides no information about the quality of the work.

However, the biggest challenges a data platform faces are rarely simple failures. They manifest as slow degradations, silent data corruptions, and gradual drifts that evade simple success/failure states. Catching these requires a system designed to answer a more nuanced set of questions: “Is this process healthy? Is the outcome correct?”

This evolution in capability represents the move from Data Monitoring to Data Observability.