AWS sagemaker-unified-studio documentation change
Summary
Added release notes for November 2025 including new features: one-click dataset onboarding, AI agent notebooks, SageMaker Data Agent, and Athena for Apache Spark integration.
Security assessment
Mentions IAM role usage and Lake Formation access controls, which are security features, but does not address a specific vulnerability. The changes primarily document new functionality with inherent security controls.
Diff
diff --git a/sagemaker-unified-studio/latest/userguide/release-notes.md b/sagemaker-unified-studio/latest/userguide/release-notes.md index b6c4a0fa9..439f94f3c 100644 --- a//sagemaker-unified-studio/latest/userguide/release-notes.md +++ b//sagemaker-unified-studio/latest/userguide/release-notes.md @@ -5 +5 @@ -September 2025August 2025July 2025June 2025 (Additional)June 2025May 2025 +November 2025September 2025August 2025July 2025June 2025 (Additional)June 2025May 2025 @@ -10,0 +11,20 @@ The following sections describe the feature releases for Amazon SageMaker Unifie +## November 2025 + +### November 21, 2025 + +**Introducing one-click onboarding of existing datasets to Amazon SageMaker** + +Amazon SageMaker Unified Studio now offers one-click onboarding that helps customers start working with their existing AWS data in minutes. Customers can start directly from Amazon SageMaker, Amazon Athena, Amazon Redshift, or Amazon S3 Tables, giving them a fast path from their existing tools and data to the simple experience in SageMaker Unified Studio. After clicking "Get Started" and specifying an AWS IAM role, SageMaker automatically creates a project with all existing data permissions intact from AWS Glue Data Catalog, AWS Lake Formation, and Amazon S3. A notebook and serverless compute are pre-configured to accelerate first use. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-sagemaker-one-click-onboarding-existing-datasets/). + +**Announcing notebooks with a built-in AI agent in Amazon SageMaker** + +The new SageMaker notebooks provide data and AI teams a high-performance, serverless programming environment for analytics and machine learning jobs. Customers can quickly get started working with data without pre-provisioning data processing infrastructure. The notebook gives data engineers, analysts, and data scientists one place to perform SQL queries, execute Python code, process large-scale data jobs, run machine learning workloads and create visualizations, without having to switch between tools. It is powered by Amazon Athena for Apache Spark, automatically scaling from interactive queries to petabyte-scale processing. A built-in AI agent accelerates development by generating code and SQL statements from natural language prompts while guiding users through their tasks. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2025/11/notebooks-built-in-ai-agent-amazon-sagemaker/). + +**Introducing Amazon SageMaker Data Agent for analytics and AI/ML development** + +SageMaker Data Agent works within new SageMaker notebooks to break down complex analytics and ML tasks into manageable steps. Customers can describe objectives in natural language and the agent creates a detailed execution plan and generates the required SQL and Python code. The agent maintains awareness of the notebook context, including available data sources, schemas, and catalog information, managing common tasks including data transformation, statistical analysis, and model development. This helps data engineers, analysts, and data scientists who spend significant time on manual setup tasks and boilerplate code build analytics and ML applications faster. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-sagemaker-data-agent-analytics-ai-ml-development/). + +**Amazon Athena for Apache Spark is now available in Amazon SageMaker notebooks** + +Amazon SageMaker now supports Amazon Athena for Apache Spark, bringing a new notebook experience and fast serverless Spark experience together within a unified workspace. Now, data engineers, analysts, and data scientists can easily query data, run Python code, develop jobs, train models, visualize data, and work with AI from one place, with no infrastructure to manage and second-level billing. Athena for Apache Spark scales in seconds to support any workload, from interactive queries to petabyte-scale jobs. Athena for Apache Spark now runs on Spark 3.5.6, the same high-performance Spark engine available across AWS, optimized for open table formats including Apache Iceberg and Delta Lake. It brings you new debugging features, real-time monitoring in the Spark UI, and secure interactive cluster communication through Spark Connect. As you use these capabilities to work with your data, Athena for Spark now enforces table-level access controls defined in AWS Lake Formation. For more information, see [What's New Post](https://aws.amazon.com/about-aws/whats-new/2025/11/amazon-athena-apache-spark-sagemaker-notebooks/). +