- 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. 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
The documentation linked to above covers getting started with Spark, as well the built-in components MLlib, Spark Streaming, and GraphX In addition, this page lists other resources for learning Spark
- 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
- 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
- Spark SQL DataFrames | Apache Spark
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 the Spark engine, which provides full mid-query fault tolerance
- 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
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