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  1. Apache Spark™ - Unified Engine for large-scale data analytics

    Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

  2. 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 …

  3. Overview - Spark 4.1.0 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.

  4. Quick Start - Spark 4.1.0 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 …

  5. Examples - Apache Spark

    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 …

  6. PySpark Overview — PySpark 4.1.0 documentation - Apache Spark

    Dec 11, 2025 · 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 …

  7. Spark SQL & DataFrames | Apache Spark

    Spark SQL includes a cost-based optimizer, columnar storage and code generation to make queries fast. At the same time, it scales to thousands of nodes and multi hour queries using the Spark …

  8. Documentation - Apache Spark

    Apache Spark™ Documentation Setup instructions, programming guides, and other documentation are available for each stable version of Spark below: Spark Spark 4.1.0

  9. Getting Started — PySpark 4.1.0 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:

  10. Structured Streaming Programming Guide - Spark 4.1.0 Documentation

    Types of time windows Spark supports three types of time windows: tumbling (fixed), sliding and session. Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time …