Data engineering·Quality, tests and knowing it is wrong
you get told that a table's volume, freshness and schema look unusual without anyone having written a test for that table.
Data observability
Also calledpipeline observability, data monitoring
Automatically monitoring datasets for anomalies in freshness, volume, schema and distribution, across everything rather than where someone thought to look. Its complement is testing, which encodes specific known expectations; observability finds the problems nobody anticipated on the tables nobody owns. It generates far more noise than tests and is worth it mainly at a table count where hand-written coverage has stopped being plausible.