Comparison
Data observabilityvsDistribution check
Data observability
you get told that a table's volume, freshness and schema look unusual without anyone having written a test for that table.
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.
Full entry →Distribution check
the average order value did not change but the shape did, because a whole segment of small orders stopped arriving.
Comparing a column's distribution — percentiles, category frequencies, cardinality — against its recent history. It catches the class of problem that passes every structural test: right types, right row count, wrong data. It is the most likely check to produce false alarms, so it works best as a warning that a human reviews rather than as something that blocks a pipeline.
Full entry →