Cloudera announced that it will reduce the operational burden on enterprise customers by leveraging its long-term support framework for its hybrid data and AI platform. The key point is to reduce the pressure for frequent upgrades by extending the end of platform support to 2032 and continuing performance improvements and expansion capabilities within the same framework.

Cloudera announced these updates in San Jose, U.S., on April 8 (U.S. West Coast Time). The company explained that it aims to provide a consistent operational experience across the cloud and data centers, and support enterprises in accelerating the adoption of data analytics and AI without large-scale migration tasks.

The background of this announcement lies in the growing infrastructure burden accompanying the expansion of AI investment. Gartner forecasts that global AI spending will reach $2.5278 trillion in 2026 and $3.3367 trillion in 2027. This reflects the awareness that while investment scale is increasing, platform replacement cycles and complex operational costs in the field are hindering innovation.

In this update, Cloudera introduced automatic optimization of Apache Iceberg tables, cloud bursting that pulls cloud resources when needed, and data sharing features that allow external platforms to access Iceberg tables in real time. According to the official announcement, Iceberg optimization aims to increase query performance by 38% and reduce storage overhead by up to 36%.

Leo Brunik, Chief Product Officer (CPO), stated that enterprise customers are seeking the flexibility of the cloud, control over data centers, and uninterrupted scalability simultaneously. According to the support policy document, the end of support for Cloudera OnCloud version 7.3.2 is scheduled for the second quarter of 2032. This announcement demonstrates that as the race to adopt AI accelerates, the ability to scale existing data environments with minimal disruption, rather than adopting new platforms, is emerging as a key variable in corporate operational strategies.