TLDR: Amazon SageMaker Catalog now supports Amazon S3 general purpose buckets and prefixes, significantly improving data discoverability and governance for data scientists, engineers, and business analysts. This integration, announced in July 2025, allows data producers to share unstructured data as ‘S3 Object Collections’ with fine-grained access controls via S3 Access Grants, streamlining workflows and fostering secure collaboration.
Amazon Web Services (AWS) has announced a significant expansion of the Amazon SageMaker Catalog, introducing support for Amazon S3 general purpose buckets and prefixes. This enhancement, initially unveiled in July 2025, aims to revolutionize how data teams manage and access their diverse datasets, particularly addressing the complexities of manual data discovery and permission management within Amazon S3 environments. The official AWS Big Data Blog published a detailed post on August 21, 2025, outlining the capabilities and benefits of this new integration.
The core of this update lies in its ability to deliver fine-grained access control and permissions through S3 Access Grants. This integration directly tackles the challenge data teams face when managing data discovery and S3 permissions as separate, often cumbersome, workflows. Data consumers, including data scientists, engineers, and business analysts, can now effortlessly discover and access S3 buckets or prefixes as data assets directly through the SageMaker Catalog. Concurrently, administrators can maintain robust, granular access controls, ensuring secure and appropriate data usage across their organizations.
Building upon SageMaker’s existing support for structured data in Amazon S3 Tables buckets, the new capability extends to unstructured data types such as documents, images, audio, and video. Data producers can now share these diverse datasets as ‘S3 Object Collections,’ enriching them with business metadata like data owner and classification terms, making them readily discoverable. Once published, these assets can be found using SageMaker’s search and browse functionalities, and consumers can subscribe to them to receive access and updates.
The benefits of this integration are multifaceted. It streamlines data-to-insights workflows, eliminates manual setup for lakehouse datasets, and fosters improved collaboration. For instance, Bayer, a prominent pharmaceutical and biotechnology company, highlighted the impact of this feature. A representative stated, “Bringing a new drug to market is widely known across the industry to be a lengthy and expensive process, often taking 10–15 years and costing $1–2 billion on average, with a low overall success rate ranging from around 8% to 12%. SageMaker now allows us to easily discover and securely access data, structured and unstructured, while maintaining governance controls using S3 Access Grants.”
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This functionality is available in all AWS Regions where Amazon SageMaker is offered, with standard SageMaker, QuickSight, and S3 pricing. The process of publishing data to the SageMaker Catalog involves navigating to a project in SageMaker Unified Studio, selecting the Amazon S3 location under the Data pane, and choosing ‘Publish to Catalog’ from the Actions dropdown menu. This strategic move by AWS underscores its commitment to unifying data engineering, analytics, and generative AI within a streamlined studio environment, enhancing capabilities for secure data management and accelerating AI innovation.
