Amazon SageMaker Unified Studio now supports data profiling and anomaly detection
Amazon SageMaker Unified Studio adds data profiling and anomaly detection using AWS Glue Data Quality, enabling users to generate statistical profiles and detect data drift without predefined thresholds.
Amazon SageMaker Unified Studio now includes data profiling and anomaly detection powered by AWS Glue Data Quality. Users can generate statistical profiles to understand data shape and completeness, and track changes over time. Anomaly detection identifies when data points drift from historical patterns without needing predefined thresholds or custom rules. These capabilities work for both static data in catalog tables and data in transit within Visual ETL jobs. A dedicated Data Profile tab offers on-demand and scheduled profiling for dataset and column-level statistics. As profile history accumulates, anomaly detection establishes a baseline of expected behavior and flags outliers. This is useful when specific thresholds are unknown or when expected values change over time. The same profiling and anomaly detection are available on the results page of any Visual ETL job with an Evaluate Data Quality transform. The feature is available in all regions where Amazon SageMaker Unified Studio is offered.
Why it matters
This update benefits data stewards, engineers, and analysts who manage and analyze data. The data profiling and anomaly detection capabilities help improve data quality monitoring and assurance.