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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
- 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
- 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. 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
- 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
- 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
- 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
- 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
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