Cloudera Teams With NVIDIA to Lower Cloud Compute Spend and Accelerate Apache Spark Pipelines

New NVIDIA GPU acceleration for Apache Spark in Cloudera Data Engineering delivers zero-code performance gains, lower cloud costs, and faster AI-ready data pipelines

SAN JOSE, Calif., Aug. 20, 2026 (GLOBE NEWSWIRE) -- Cloudera, the only company bringing AI to data anywhere, today announced native GPU acceleration for Apache Spark 4.1 in Cloudera Data Engineering, enabled by the NVIDIA CUDA-X library, cuDF. The NVIDIA cuDF plug-in for Apache Spark will support the just-announced Cloudera Anywhere Cloud™, which is designed to enable organizations to accelerate Spark workloads without rewriting PySpark or SQL code. This helps data teams prepare AI-ready data faster while reducing cloud infrastructure costs across hybrid environments.

As organizations expand AI initiatives, the speed of data preparation has become a critical challenge. Large-scale Spark workloads often take hours to complete, delaying analytics and AI applications while driving up cloud compute costs. By embedding GPU acceleration directly into Cloudera Data Engineering, organizations can dramatically reduce processing times using their existing Spark applications, without changing code or operational workflows.

Accelerating Spark for Enterprise AI
Apache Spark powers many of today's enterprise data pipelines. With native GPU acceleration built into Cloudera Data Engineering, organizations can improve performance while maintaining the security and governance required for production workloads. Leveraging NVIDIA cuDF for Spark workloads, Cloudera will provide up to 4x workload acceleration on NVIDIA GPUs compared to traditional CPU infrastructure, enabling enterprises to drastically shorten data processing cycles.

Together, Cloudera Data Engineering accelerated by NVIDIA CUDA-X™ libraries will deliver:

• Zero-code GPU acceleration for Apache Spark 4.1 workloads
• Faster ETL and data preparation for analytics and AI
• Lower cloud infrastructure costs through shorter compute runtimes
• Built-in deployment with no manual driver configuration
• Enterprise security and governance through the Cloudera Unified Data Fabric
• Consistent performance across public cloud, private cloud, sovereign cloud, and on-premises environments

Unlike GPU acceleration offerings limited to a single cloud provider, Cloudera extends these capabilities across hybrid environments while preserving consistent governance and operations. Organizations can accelerate Spark workloads wherever their data resides— whether that is on premises, in the clouds, or at the edge— without sacrificing security or flexibility.

Faster Data Pipelines, Lower Infrastructure Costs
Preparing trusted data quickly, and at a reasonable cost, is essential for analytics and AI. Recent data from Cloudera's The Great Re-Architecture Survey found that the vast majority (84%) of respondents said that AI workloads have caused infrastructure costs to increase. By accelerating Spark processing, organizations can deliver clean, model-ready data faster, helping to reduce infrastructure costs while improving productivity across data engineering, AI, and analytics teams.

"For many organizations, AI isn't limited by models. It's limited by how quickly they can turn raw data into trusted, usable insights," said Leo Brunnick, Chief Product Officer at Cloudera. "Accelerating Spark inside Cloudera Data Engineering helps remove that bottleneck, allowing customers to move from data preparation to analytics and AI faster while keeping governance, security, and operational consistency at the center of their strategy."

"The fastest path to accelerating AI deployments is the one that aligns with how enterprises already operate today," said Pat Lee, vice president, Strategic Enterprise Partnerships at NVIDIA. "With NVIDIA AI infrastructure and CUDA-X libraries now native to Cloudera Data Engineering, enterprises can lower costs and dramatically speed up Apache Spark pipelines without changing a single line of PySpark or SQL code, turning business data into a foundation for AI."

The GPU acceleration capability for Apache Spark will be available in Cloudera Data Engineering as part of the Cloudera Anywhere Cloud^TM announced at EVOLVE Singapore on August 20, 2026. Additional demonstrations and technical sessions will be featured at NVIDIA GTC Berlin and Cloudera EVOLVE New York later this year.

To learn more, visit Cloudera.com or register for upcoming EVOLVE conferences to see how organizations are accelerating data engineering and AI across hybrid environments.

About Cloudera

Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, sovereign clouds, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives.

To learn more, visit Cloudera.com and follow us on LinkedIn and X. ©2026 Cloudera and associated marks are trademarks or registered trademarks of Cloudera, Inc. All other company and product names are trademarks of their respective owners.  

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Jess Hohn-Cabana
cloudera@v2comms.com