Amazon EMR
Amazon EMR Release Guide

What's New?

This topic covers features and issues resolved in the current release of Amazon EMR. These release notes are also available on the Release 5.27.0 Tab, along with the application versions, component versions, and available configuration classifications for this release.

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For earlier release notes going back to release version 4.2.0, see Amazon EMR What's New History.

Release 5.27.0 (Latest)

New Amazon EMR release versions are made available in different regions over a period of several days, beginning with the first region on the initial release date. The latest release version may not be available in your region during this period.

The following release notes include information for Amazon EMR release version 5.27.0. Changes are relative to 5.26.0.

Initial release date: Sep 23, 2019


  • AWS SDK for Java 1.11.615

  • Flink 1.8.1

  • JupyterHub 1.0.0

  • Spark 2.4.4

  • Tensorflow 1.14.0

  • Connectors and drivers:

    • DynamoDB Connector 4.12.0

New Features

  • (Oct 24, 2019) The following new features in EMR notebooks are available with all Amazon EMR releases.

    • You can now associate Git repositories with EMR notebooks to store your notebooks in a version controlled environment. You can share code with peers and reuse existing Jupyter notebooks through remote Git repositories. For more information, see Associate Git Repositories with Amazon EMR Notebooks in the Amazon EMR Management Guide.

    • The nbdime utility is now available in EMR notebooks to simplify comparing and merging notebooks.  

    • EMR notebooks now support JupyterLab. JupyterLab is a web-based interactive development environment fully compatible with Jupyter notebooks. You can now choose to open your notebook in either JupyterLab or Jupyter notebook editor. 

  • (Oct 30, 2019) With Amazon EMR versions 5.25.0 and later, you can connect to Spark history server UI from the cluster Summary page or the Application history tab in the console. Instead of setting up a web proxy through an SSH connection, you can quickly access the Spark history server UI to view application metrics and access relevant log files for active and terminated clusters. For more information, see Access Spark History Server UI from the Console in the Amazon EMR Management Guide.

Changes, Enhancements, and Resolved Issues

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