|
- 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
- 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
- Documentation | Apache Spark
Apache Spark™ Documentation Setup instructions, programming guides, and other documentation are available for each stable version of Spark below: Spark
- 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 and patching together various libraries to perform an analysis
- 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:
|
|
|