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Lightning-fast unified analytics engine.

Apache Spark achieves high performance for both batch and streaming Spark on line, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. You can combine these libraries seamlessly in the same application.

Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.

Spark is used at a wide range of organizations Spark on line process large datasets. You can find many example use cases on the Powered By page.

Apache Spark is built by a wide set of developers from over companies. Sincemore than developers have contributed to Spark!

The project's committers come from more than 25 organizations. If you'd like to participate in Spark, or contribute to the libraries on top of it, learn how to contribute. Toggle navigation.

Download Spark Built-in Libraries: Speed Run workloads x faster. Logistic regression in Hadoop and Spark.

Generality Combine SQL, streaming, and complex analytics. Community Spark is used at a wide range of organizations to process large datasets.

There are many ways to reach the community: Use the mailing lists to ask questions. In-person events include numerous meetup groups and conferences.

We use JIRA for issue tracking. Contributors Apache Spark is built by a wide set of developers from over companies.

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Download the latest release: Read the quick start guide. Learn how to deploy Spark on a cluster.