NVIDIA Accelerates Apache Spark, World’s Leading Data Analytics Platform

Open Source Community Accelerates Spark 3.0 with Native NVIDIA GPU Support;
Lightning-Fast ETL and SQL Processing on Hundreds of Terabytes of Data;
Adobe Achieves 7x Speedup in Model Training with Spark 3.0 on Databricks

SANTA CLARA, Calif., May 14, 2020 (GLOBE NEWSWIRE) -- NVIDIA today announced that it is collaborating with the open-source community to bring end-to-end GPU acceleration to Apache Spark 3.0, an analytics engine for big data processing used by more than 500,000 data scientists worldwide.

With the anticipated late spring release of Spark 3.0, data scientists and machine learning engineers will for the first time be able to apply revolutionary GPU acceleration to the ETL (extract, transform and load) data processing workloads widely conducted using SQL database operations.

In another first, AI model training will be able to be processed on the same Spark cluster, instead of running the workloads as separate processes on separate infrastructure. This enables high-performance data analytics across the entire data science pipeline, accelerating tens to thousands of terabytes of data from data lake to model training, without changes to existing code used for Spark applications running on premises and in the cloud.

“Data analytics is the greatest high performance computing challenge facing today’s enterprises and researchers,” said Manuvir Das, head of Enterprise Computing at NVIDIA. “Native GPU acceleration for the entire Spark 3.0 pipeline — from ETL to training to inference — delivers the performance and scale needed to finally connect the potential of big data with the power of AI.”

Building on its strategic AI partnership with NVIDIA, Adobe is one of the first companies working with a preview release of Spark 3.0 running on Databricks. It has achieved a 7x performance improvement and 90 percent cost savings in an initial test, using GPU-accelerated data analytics for product development in Adobe Experience Cloud and supporting features that power digital businesses.

The performance gains in Spark 3.0 enhance model accuracy by enabling scientists to train models with larger datasets and retrain models more frequently. This makes it possible to process terabytes of new data every day, which is critical for data scientists supporting online recommender systems or analyzing new research data. In addition, faster processing means that fewer hardware resources are needed to deliver results, providing significant cost savings.