Amazon S3 Tables integration with SageMaker Lakehouse is now generally available

Amazon S3 Tables now seamlessly integrate with Amazon SageMaker Lakehouse, making it easy to query and join S3 Tables with data in S3 data lakes, Amazon Redshift data warehouses, and third-party data sources. S3 Tables deliver the first cloud object store with built-in Apache Iceberg support. SageMaker Lakehouse is a unified, open, and secure data lakehouse that simplifies your analytics and artificial intelligence (AI). All data in SageMaker Lakehouse can be queried from SageMaker Unified Studio and engines such as Amazon EMR, AWS Glue, Amazon Redshift, Amazon Athena, and Apache Iceberg-compatible engines like Apache Spark or PyIceberg.

SageMaker Lakehouse provides the flexibility to access and query data in-place across S3 Tables, S3 buckets, and Redshift warehouses using the Apache Iceberg open standard. You can secure and centrally manage your data in the lakehouse by defining fine-grained permissions that are consistently applied across all analytics and ML tools and engines. You can access SageMaker Lakehouse from Amazon SageMaker Unified Studio, a single data and AI development environment that brings together functionality and tools from AWS analytics and AI/ML services.

The integrated experience to access S3 Tables with SageMaker Lakehouse is generally available in all AWS Regions where S3 Tables are available. To get started, enable S3 Tables integration with Amazon SageMaker Lakehouse, which allows AWS analytics services to automatically discover and access your S3 Tables data. To learn more about S3 Tables integration, visit the documentation and product page. To learn more about SageMaker Lakehouse, visit the documentation, product page, and read the launch blog.