If you’d like to build Spark from source, visit Building Spark. Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS), and it should run on any platform that runs a supported version of Java.
Spark docker images are available from Dockerhub under the accounts of both The Apache Software Foundation and Official Images. Note that, these images contain non-ASF software and may be subject to different license terms.
To follow along with this guide, first, download a packaged release of Spark from the Spark website. Since we won’t be using HDFS, you can download a package for any version of Hadoop.
Spark allows you to perform DataFrame operations with programmatic APIs, write SQL, perform streaming analyses, and do machine learning. Spark saves you from learning multiple frameworks and patching together various libraries to perform an analysis.
Spark Connect is a client-server architecture within Apache Spark that enables remote connectivity to Spark clusters from any application. PySpark provides the client for the Spark Connect server, allowing Spark to be used as a service.
Apache Spark™ Documentation Setup instructions, programming guides, and other documentation are available for each stable version of Spark below: Spark
There are more guides shared with other languages such as Quick Start in Programming Guides at the Spark documentation. There are live notebooks where you can try PySpark out without any other step:
Apache Spark 4.0.0 marks a significant milestone as the inaugural release in the 4.x series, embodying the collective effort of the vibrant open-source community.
Spark 3.5.5 released We are happy to announce the availability of Spark 3.5.5! Visit the release notes to read about the new features, or download the release today. Spark News Archive
