AWS Glue Data Quality now supports anomaly detection and writing results to the AWS Glue Data Catalog

AWS Glue Data Quality now supports anomaly detection for Catalog-based evaluations and writes results to the AWS Glue Data Catalog, providing consistent data quality monitoring across ETL jobs and Catalog evaluations.

• Anomaly detection uses ML-powered time-series forecasting to identify unexpected data changes without explicit thresholds
• Automatically surface issues like sudden drops in distinct values or row count spikes
• Evaluation results, profiling metrics, and anomaly predictions stored in queryable GDC tables
• Results accessible via standard SQL queries at any time
• Available in all AWS commercial regions and AWS GovCloud (US)

These capabilities enable data engineers to efficiently monitor hundreds of tables and maintain data quality across diverse workflows.