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- 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
- 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
- Quick Start - Spark 4. 0. 1 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
- 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
- Configuration - Spark 4. 0. 1 Documentation
Spark provides three locations to configure the system: Spark properties control most application parameters and can be set by using a SparkConf object, or through Java system properties Environment variables can be used to set per-machine settings, such as the IP address, through the conf spark-env sh script on each node
- RDD Programming Guide - Spark 4. 0. 1 Documentation
Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only “added” to, such as counters and sums This guide shows each of these features in each of Spark’s supported languages
- Running Spark on Kubernetes - Spark 4. 0. 1 Documentation
Spark executors must be able to connect to the Spark driver over a hostname and a port that is routable from the Spark executors The specific network configuration that will be required for Spark to work in client mode will vary per setup
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