Data Observability
Freshness, volume, schema, and quality for the tables your pipelines land — backed by Databricks Unity Catalog
Tables monitored
1,284
Passing
94.1%
Stale tables
23
Schema changes (7d)
11
Breaking changes needing review
How tables are scored
Every monitored table is scored on the same four checks, so one number is comparable across catalogs.
- Freshness— time since the last successful write
- Volume— row count against its own recent baseline
- Schema— added, dropped, or retyped columns
- Quality— null rates and distribution drift on tracked columns
Monitored tables
Unity Catalog — catalog.schema.table, scanned on the schedule each organization configures
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