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Databricks, the inaugural partner of Cloudera Connect: Innovators, teams with Cloudera for High-Speed Data Analytics

PALO ALTO, Calif. and NEW YORK, NYOctober 28, 2013 – At the Strata Conference + Hadoop World 2013Cloudera, the leader in enterprise analytic data management powered by Apache Hadoop™, today announced its new Cloudera Connect: Innovators program to help customers harness the latest innovations from projects and companies, while getting the reliability and production support they've come to expect from Cloudera.

The program's charter partner, Databricks, which was spun out of AMPLab at the University of California (UC), Berkeley, is the company behind the popular Apache Spark framework. Concurrently, Cloudera also announced direct support for Apache Spark with CDH, Cloudera's market-leading distribution including Apache Hadoop. With Apache Spark, Cloudera users can now perform rapid, resilient processing of in-memory datasets stored in Hadoop, as well as general data processing.

Together with Cloudera Impala for interactive SQL workloads, Cloudera Search for interactive full text search, and Cloudera Enterprise 5 -- which includes the latest innovations from Hadoop 2, as well as new high performance, efficiency, and data management enhancements -- Cloudera is now further enabling customers to choose the right tool for any big data workload, and helping them succeed in using data to solve business problems as quickly and effectively as possible.

"Cloudera is a leader in enterprise analytic data management and the company's new Innovators program is a great opportunity for us to benefit from their world-class expertise in the enterprise market to bring Spark to new users," said Ion Stoica, chief executive officer, Databricks. "We look forward to collaborating with them on Spark and helping our mutual customers do more with their data."

Cloudera + Apache Spark: Lightning-Fast Data Processing for Hadoop

Originally developed by the AMPLab at the UC Berkeley, Spark is a cluster computing system and execution engine that supports high-speed data analytics. Spark provides a strong complement to Hadoop and is well suited to perform high-speed data processing with the ability to run complex computations up to 100x faster than MapReduce. Spark gets this speed and power from its generic execution model, which optimizes arbitrary operator graphs, and from its ability to process data in-memory rather than always reading it from disk. In addition, Spark provides powerful and easy to use APIs in Scala, Java and Python, which dramatically reduces development time. Combined with facilities to provide fault-tolerance, Spark is an exciting alternative framework for Hadoop.

"No one company by itself can develop all the innovation that enterprises require. The Cloudera Connect: Innovators program will allow our customers to tap into the best innovations the open source community has to offer, whether that innovation was developed by Cloudera or not," said Charles Zedlewski, vice president, products at Cloudera. "Apache Spark is a prime example. It provides excellent data processing functionality and performance and has a vibrant developer and user community."

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About Cloudera

Cloudera is revolutionizing enterprise data management by offering the first unified Platform for big data, an enterprise data hub built on Apache Hadoop. Cloudera offers enterprises one place to store, access, process, secure, and analyze all their data, empowering them to extend the value of existing investments while enabling fundamental new ways to derive value from their data. Cloudera's open source big data platform is the most widely adopted in the world, and Cloudera is the most prolific contributor to the open source Hadoop ecosystem. As the leading educator of Hadoop professionals, Cloudera has trained over 40,000 individuals worldwide. Over 1,700 partners and a seasoned professional services team help deliver greater time to value. Leading organizations in every industry plus top public sector organizations globally run Cloudera in production.

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