Discover how Amazon SageMaker Unified Studio surfaces data quality metrics from multiple tools including AWS Glue Data Quality in one unified view, enabling data consumers to gain trust in datasets before subscribing. In this demo, learn how to view overall quality scores with individual rule results, examine historical data quality trends to detect drift over time, and understand which specific data issues like missing values, duplicates, or stale data need attention. See how data engineers can author data quality rules directly in Unified Studio using the Data Quality Definition Language with searchable rule types and syntax examples, then schedule rules to run automatically without switching to the AWS Console. Discover how quality scores flow immediately to the catalog listing so all consumers can see them without republishing, and explore how to track rule-level details and historical trends even before subscribing to a dataset.
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