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Yandex Data Processing
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In this article:

  • December 2024
  • September 2024
  • April 2024
  • Q2 2023
  • Q3 2022
  • Q2 2022
  • Q1 2022
  1. Release notes
  2. Service updates

Yandex Data Processing release notes

Written by
Yandex Cloud
Updated at April 10, 2025
  • December 2024
  • September 2024
  • April 2024
  • Q2 2023
  • Q3 2022
  • Q2 2022
  • Q1 2022

December 2024December 2024

When creating or editing a cluster, you can now select the environment: PRODUCTION or PRESTABLE.

September 2024September 2024

Metastore clusters are now part of Yandex MetaData Hub. For information on Metastore clusters, see the Yandex MetaData Hub documentation.

April 2024April 2024

A stable line of 2.1 images is available. With it, you can create a cluster with more recent Spark 3.3.2 and Hadoop 3.3.2 versions.

Q2 2023Q2 2023

Creating Metastore clusters is now available. This feature is at the Preview stage.

Q3 2022Q3 2022

  • Added support for new settings in the DataprocCreateClusterOperator Airflow operator.
  • Added cpu-optimized host classes with 2:1 GB RAM to vCPU ratio. The new configurations are only available for Intel Ice Lake.
  • Published a guide for using initialization scripts to set up GeeseFS.

Q2 2022Q2 2022

  • Image version 2.1 available.
  • Added the ability to enable public internet access for subclusters of all types.
  • Lightweight Spark is available starting with image version 2.0.39. You can now create a cluster without data storage subclusters because YARN and SPARK services are no longer dependent on HDFS.
  • Added support for initialization scripts in the CLI.

Q1 2022Q1 2022

  • You can now create clusters on non-replicated network drives up to 8 TB. Non-replicated drives are much simpler than standard network SSD storage, which makes them perform several times faster.
  • Added the ability to cancel a job.
  • Added the build number in image version Yandex Data Processing.
  • Added the ability to provide the packages, repositories, and exclude_packages parameters for Spark and PySpark jobs. By using these parameters, you can download additional dependencies and packages from external repositories.

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