Category: Amazon SageMaker Data & AI Governance

Amazon SageMaker Catalog now supports metadata enforcement rules for glossary terms classification (tagging) at the asset level. With this capability, administrators can require that assets include specific business terms or classifications. Data producers must apply required glossary terms or classifications before an asset can be published. In this post, we show how to enforce business glossary classification rules in SageMaker Catalog.

Amazon SageMaker Catalog now supports custom metadata forms and rich text descriptions at the column level, extending existing curation capabilities for business names, descriptions, and glossary term classifications. Column-level context is essential for understanding and trusting data. This release helps organizations improve data discoverability, collaboration, and governance by letting metadata stewards document columns using structured and formatted information that aligns with internal standards. In this post, we show how to enhance data discovery in SageMaker Catalog with custom metadata forms and rich text documentation at the schema level.

In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. You’ll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation.

Organizations need to efficiently manage data assets while maintaining governance controls in their data marketplaces. Although manual approval workflows remain important for sensitive datasets and production systems, there’s an increasing need for automated approval processes with less sensitive datasets. In this post, we show you how to automate subscription request approvals within SageMaker, accelerating data access for data consumers.

In this post, we show you how to create custom notifications for events occurring in SageMaker Catalog using Amazon EventBridge, AWS Lambda, and Amazon SNS. You can expand this solution to automatically integrate SageMaker Catalog with in-house enterprise workflow tools like ServiceNow and Helix.

Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including […]

Security and compliance concerns are key considerations when customers across industries rely on Amazon SageMaker Catalog. Customers use SageMaker Catalog to organize, discover, and govern data and machine learning (ML) assets. A common request from domain administrators is the ability to enforce governance controls on certain metadata terms that carry compliance or policy significance. Examples […]

In this post, we demonstrate how to use Amazon Bedrock Flows in SageMaker Unified Studio to build a sophisticated generative AI application for financial analysis and investment decision-making.

In this post, we explore its real-world impact through the lens of an ecommerce company striving to boost their bottom line. To illustrate this practical application, we walk you through how you can use the prebuilt integration between SageMaker Catalog and AWS Glue crawlers to automatically capture lineage for data assets stored in Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.

Today, we’re announcing the open source release of Spark History Server MCP, a specialized Model Context Protocol (MCP) server that transforms this workflow by enabling AI assistants to access and analyze your existing Spark History Server data through natural language interactions. This project, developed collaboratively by AWS open source and Amazon SageMaker Data Processing, turns complex debugging sessions into conversational interactions that deliver faster, more accurate insights without requiring changes to your current Spark infrastructure. You can use this MCP server with your self-managed or AWS managed Spark History Servers to analyze Spark applications running in the cloud or on-premises deployments.