AWS Glue Data Quality extended its functionality to include machine learning-powered anomaly detection for data quality evaluations on Glue Data Catalog tables. This allows detection of unexpected changes in data statistics, such as sudden shifts in distinct values or row counts, without manual thresholds.
Furthermore, rule outcomes, profiling metrics, and anomaly predictions are now stored directly in Glue Data Catalog tables, making these results easily queryable via SQL across different evaluation workflows. This integration facilitates consistent data quality insights and historic tracking within AWS Glue environments.
Source & further reading
AWS Glue Data Quality now supports anomaly detection and writing results to the AWS Glue Data Catalog ↗Source published 2026-07-27. This report was prepared with AI assistance from the linked primary-source material. Claims and limitations should be checked against the original announcement or research.