Overview

Quality Monitoring detects issues such as data freshness delays, unusual data patterns, and changes in column-level metrics before they affect dashboards, machine learning models, or other downstream systems. It alerts you to potential problems and provides context to trace them back to upstream jobs or sources.

Key capabilities

With Quality Monitoring, you can:

• Detect delayed updates and unexpected row count behavior in your tables
• Surface changes in column-level metrics such as null counts or uniqueness
• Set up monitors using static thresholds or historical baselines
• Trace quality issues using lineage views that show upstream jobs and downstream impact

Supported data sources

Further reading

Additional helpful documentation, links, and articles:

Data ObservabilityDOCUMENTATION Jobs MonitoringDOCUMENTATION