Amazon SageMaker Catalog now supports read and write access to Amazon S3

Amazon SageMaker Catalog now supports read and write access to Amazon S3 general purpose buckets. This capability helps data scientists and analysts search for unstructured data, process it alongside structured datasets, and share transformed datasets with other teams. Data publishers gain additional controls to support analytics and generative AI workflows within SageMaker Unified Studio while maintaining security and governance controls over shared data. When approving subscription requests or directly sharing S3 data within the SageMaker Catalog, data producers can choose to grant read-only or read and write access. If granted read and write access, data consumers can process datasets in SageMaker and store the results back to the S3 bucket or folder. The data can then be published and automatically discoverable by other teams. This capability is now available in [all AWS Regions](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html) where Amazon SageMaker Unified Studio is supported. To get started, you can log into SageMaker Unified Studio, or you can use the Amazon DataZone API, SDK, or AWS CLI. To learn more, see the [SageMaker Unified Studio guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/data-s3.html).