Accelerate your data quality journey for lakehouse architecture with Amazon SageMaker, Apache Iceberg on AWS, Amazon S3 tables, and AWS Glue Data Quality

This article discusses how to accelerate data quality workflows using AWS Glue Data Quality integrated with Amazon SageMaker, Apache Iceberg, and Amazon S3 tables. The solution provides a comprehensive approach to measuring and monitoring data quality in a lakehouse architecture.

• Enables measuring data quality through AWS Glue Data Quality features
• Supports data quality checks on Apache Iceberg tables and Amazon S3 tables
• Provides automated data quality rule recommendations
• Integrates data quality results with Amazon SageMaker Unified Studio
• Allows visualization and analysis of data quality metrics

The walkthrough demonstrates how to create tables, generate data quality recommendations, run quality checks, and view results using AWS services, helping organizations ensure data reliability and trust in their analytics and machine learning workflows.