Cloudera Data Engineering Runtime end of support for Spark
Cloudera Data Engineering Runtime specifies end of support dates for Spark based on specific Cloudera Base on premises versions, including designated long-term support and deprecated version timelines.
All future dates are provided for planning purposes only and are subject to change. In each case, the projected end of support date can be considered to be the last day of the month specified in thefollowing table:
| Spark version | Cloudera Base on premises support version | End of support | Long-term support | Notes |
|---|---|---|---|---|
| Spark 2.4.8 | Up to Cloudera Base on premises 7.1.9 | September 2028 | Yes | Deprecated (will only receive bug fixes and security patches) |
| Spark 3.3.x | Up to Cloudera Base on premises 7.1.9 | August 2026 | No | Deprecated (will only receive bug fixes and security patches until August 2026) |
| Spark 3.5.x | In Cloudera Base on premises 7.1.9 and higher versions | Will follow Cloudera Base on premises support policy | Yes |
In the Support lifecycle policy page, go to to see the end of support information about Cloudera Base on premises versions and also the end of support for Spark 3.3 version.
Frequently Asked Questions (FAQs)
What is the relationship between Cloudera Data Engineering Spark runtimes and the Cloudera Base on premises version?
Cloudera Data Engineering in Cloudera Data Services on premises matches the Spark runtime availability and support provided in Cloudera Base on premises. To achieve this, Cloudera Data Engineering is certified against one or more Cloudera Base on premises versions. This ensures full compatibility between the Spark engine and the underlying platform capabilities, such as the Hive Catalog service and Ranger for security policies. Each Spark version is compatible with a Cloudera Base on premises version. For more information, see Compatibility for Cloudera Data Engineering and Cloudera Runtime components.
Does Cloudera Data Engineering offer long-term support releases?
Cloudera Data Engineering offers long-term support through underlying Spark runtimes. When running Spark jobs within Cloudera Data Engineering, you have the option to select any Spark version. Selecting specific versions of Spark designated for long-term support, allows you to continue running Spark jobs without any code changes. Because Cloudera Data Engineering job management APIs remain backward compatible, your existing automations are not impacted.
What is the end of support timeline for Spark runtimes designated long-term support?
Spark runtimes designated for long-term support in Cloudera Base on premises are certified across multiple on premises versions and are limited to minor enhancements, critical bug fixes, and security enhancements. To use a Spark runtime tagged for long-term support, you must run a supported Cloudera Data Engineering version with a corresponding certified and currently supported Cloudera Base on premises version. Within the same minor version of Cloudera Base on premises ( higher patch versions are periodically released to replace lower patch version as the designated long-term support version. Customers are automatically upgraded to these higher patch versions without any code changes.
What is the end of support timeline for non-long-term support Spark runtimes?
Spark runtimes that are not designated for long-term support will be limited to the support lifetime of the corresponding certified Cloudera Base on premises version. For more information, see Compatibility for Cloudera Data Engineering and Cloudera Runtime components.
What is the support timeline for a given Cloudera Base on premises version?
See the Cloudera platform (formerly, Cloudera Data Platform [CDP]) section in Support lifecycle policy.
