AWS announces Amazon SageMaker Lakehouse

AWS announces Amazon SageMaker Lakehouse, a unified, open, and secure data lakehouse that simplifies your analytics and artificial intelligence (AI). Amazon SageMaker Lakehouse unifies all your data across Amazon S3 data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data.

SageMaker Lakehouse gives you the flexibility to access and query your data in-place with Apache Iceberg open standard. All data in SageMaker Lakehouse can be queried from SageMaker Unified Studio (preview) and engines such as Amazon EMR, AWS Glue, Amazon Redshift or Apache Spark. You can secure your data in the lakehouse by defining fine-grained permissions, which are consistently applied across all analytics and ML tools and engines. With SageMaker Lakehouse, you can use your existing investments. You can seamlessly make data from your Redshift data warehouses available for analytics and AI/ML. In addition, you can now create data lakes by leveraging the analytics optimized Redshift Managed Storage (RMS). Bringing data into lakehouse is easy. You can use zero-ETL to bring data from operational databases, streaming services, and applications, or query in-place data via federated query.

SageMaker Lakehouse is available in US East (N. Virginia), US East (Ohio), Europe (Ireland), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (Stockholm), Europe (London), Asia Pacific (Sydney), Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Seoul), South America (Sao Paulo).

SageMaker Lakehouse is accessible directly from SageMaker Unified Studio. In addition, you can access SageMaker Lakehouse from AWS Console, AWS Glue APIs and CLIs. To learn more, visit SageMaker Lakehouse and read the launch blog. For pricing information please visit here.