- 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
- 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
- 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
- Quick Start - Spark 4. 1. 0 Documentation
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
- 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
- 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. 1. 0 documentation - Apache Spark
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 PySpark supports all of Spark’s features such as Spark SQL, DataFrames, Structured Streaming, Machine Learning (MLlib), Pipelines and Spark Core
- 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:
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