Amazon SageMaker now supports data processing jobs
AWS announces support for data processing jobs in Amazon SageMaker. This launch enables you to author, manage, monitor, and troubleshoot data processing workloads across your organization and collaborate in projects to securely build and share data processing jobs and workflows.
Amazon SageMaker Unified Studio is a single data and AI development environment where you can find and access all of the data in your organization and act on it using the best tools across any use case. With this launch, you can now build Apache Spark jobs to process large volumes of data. Jobs can be built using your preferred tool. For example, you can create jobs from Extract, Transform, and Load (ETL) scripts coded in the Unified Studio code editor, or interactively in a Unified Studio Notebooks. You can also create jobs visually using the Unified Studio Visual ETL editor. Once created, data processing jobs can be set to run on demand, scheduled using the built in scheduler, or orchestrated with SageMaker workflows. You can monitor the status of your data processing jobs and view run history showing status, logs, and performance metrics. When jobs fail, you can use generative AI troubleshooting to automatically analyze job metadata and logs, providing detailed insights that identify root causes and actionable recommendations to resolve issues quickly. Together, these capabilities enable you to author, manage, monitor, and troubleshoot data processing workloads across your organization.
See Supported Regions for a list of AWS Regions where SageMaker Unified Studio is generally available. To learn more about SageMaker Unified Studio, see the Amazon SageMaker Unified Studio webpage or documentation. You can start using SageMaker Unified Studio today by selecting “Amazon SageMaker” in the AWS Console.