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Cloudera on Cloud: August 2026 Release Summary

The Release Summary of Cloudera on cloud summarizes major features introduced in Management Console, Data Hub, and data services.

Cloudera AI

Cloudera AI Workbench 2.0.58-h2000-b10, Cloudera AI Registry 1.16.0-h2000-b5, and Cloudera AI Inference service 1.13.0-h2000-b17 provide resolutions for identified bugs.

For more information about the Known issues, Fixed issues and Behavioral changes, see the Cloudera AI Release Notes.

ML Runtimes

ML Runtimes 2026.08.1 introduces the following changes:

  • Data Connections support on Hardened runtimes: Data Connections are now fully supported on Python 3.11 and Python 3.14 Hardened runtime variants, allowing secure data connectivity for hardened runtime environments.
  • Ranger RAZ client integration (raz-client): The raz-client package has been added to Python 3.14 Hardened runtime variant.
  • R line version upgrade: Upgraded the R runtime line to support newer language features and library compatibility. Added PBJ Workbench R 4.6 Standard as the default standard R runtime.

For more information about the Known issues, Fixed issues and Behavioral changes, see the ML Runtimes Release Notes.

Cloudera Management Console

This release of Cloudera Management Console introduces the following changes:

Cloudera Lakehouse Optimizer

Task prioritization feature support
You can define a priority level for maintenance tasks that are configured for a policy. Higher-priority tasks are scheduled before lower-priority tasks when multiple policies compete for cluster resources. For more information, see Cloudera Lakehouse Optimizer features.

High availability feature support
Cloudera Lakehouse Optimizer supports high availability with multiple service instances, automatic leader election, and failover. At any time, one instance is the leader that accepts API requests and performs state-mutating operations. Standby instances take over automatically when the leader fails. For more information, see High availability for Cloudera Lakehouse Optimizer.

Puffin statistics feature support
You can create a custom policy that computes NDV statistics for Iceberg table columns and writes them to a Puffin statistics file registered in the table metadata. Query engines that read Iceberg Puffin files use these estimates for cost-based optimization. For more information, see Puffin statistics for Cloudera Lakehouse Optimizer.

Auditing feature support
Cloudera Lakehouse Optimizer provides an audit trail of user and system activities, including policy creation, editing, deletion, association, namespace operations, authentication events, and table maintenance actions. For more information, see Auditing for Cloudera Lakehouse Optimizer.