Apache Spark™ is a unified analytics engine for large-scale data processing.

Apache Spark achieves high performance for both batch and streaming data, 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. And you can use it interactively from the Scala, Python, R, and SQL shells.

Spark powers a stack of libraries including SQL and DataFrames , MLlib for machine learning, GraphX , and Spark Streaming . 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.

Community Spark is used at a wide range of organizations to process large datasets. You can find many example use cases on the Powered By page. 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 300 companies. Since 2009, more than 1200 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.