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

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution …

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 …

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

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 …

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 …

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.

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 …

PySpark combines Python’s learnability and ease of use with the power of Apache Spark to enable processing and analysis of data at any size for everyone familiar with Python.

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 …