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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
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
Spark SQL and DataFrames - Spark 4. 1. 0 Documentation Spark SQL is a Spark module for structured data processing Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed
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:
Structured Streaming Programming Guide - Spark 4. 1. 0 Documentation Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine You can express your streaming computation the same way you would express a batch computation on static data
Structured Streaming Programming Guide - Spark 4. 1. 0 Documentation Types of time windows Spark supports three types of time windows: tumbling (fixed), sliding and session Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals An input can only be bound to a single window