Overview - Spark 4. 0. 1 Documentation 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
Downloads - Apache Spark 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
Quick Start - Spark 4. 0. 1 Documentation Spark’s shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) or Python
PySpark Overview — PySpark 4. 0. 1 documentation - Apache Spark 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
Getting Started — PySpark 4. 0. 1 documentation - Apache 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:
SparkR (R on Spark) - Spark 4. 0. 1 Documentation To use Arrow when executing these, users need to set the Spark configuration ‘spark sql execution arrow sparkr enabled’ to ‘true’ first This is disabled by default
Structured Streaming Programming Guide - Spark 4. 0. 1 Documentation Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine You can express your streaming computation the same way you would express a batch computation on static data