Amazon SageMaker Unified Studio Integrates EMR Serverless for Flexible Spark Runtime

AWS enhanced SageMaker Unified Studio Notebooks by adding support for Apache Spark Connect with EMR Serverless, enabling data engineers to select Spark runtimes suited to their workloads.

By Daniel Brooks · Editorial pseudonym · 2026-06-09 · 1 min read

AI-ASSISTED REPORTING · RETROSPECTIVE ARCHIVE

Retrospective edition: 2026-06-09. Source published: 2026-06-09. Added to this archive: 2026-11-03. This briefing was prepared later from the linked announcement; it was not published on this website on the historical edition date.

Amazon SageMaker Unified Studio Notebooks now permit users to run PySpark and Spark SQL workloads using Amazon EMR Serverless alongside the existing Amazon Athena Spark runtimes. This capability offers flexibility in engine choice, facilitating interactive analytics and data engineering workflows with optimized Spark runtimes per task. Users select the desired runtime from a Notebook panel, affecting both Python and SQL environments, while also leveraging SageMaker's AI assistant for code generation and execution planning.

EMR Serverless integration supports VPC connectivity for secure, isolated workloads, pre-initialized capacity to reduce session startup time, and unified Spark UI monitoring for consistent observability across engines. This feature is available in all Regions supporting SageMaker Unified Studio and compatible with JupyterLab IDE environments.

Source & further reading

Amazon SageMaker Unified Studio Notebooks now support EMR Serverless ↗

Source published 2026-06-09. This report was prepared with AI assistance from the linked primary-source material. Claims and limitations should be checked against the original announcement or research.