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Overview - Spark 4. 0. 0 Documentation It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing
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
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. 0. 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) and Spark Core
Spark SQL DataFrames | Apache Spark Integrated Seamlessly mix SQL queries with Spark programs Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API Usable in Java, Scala, Python and R
Spark 4. 0. 0 released - Apache Spark Spark 4 0 0 released We are happy to announce the availability of Spark 4 0 0! Visit the release notes to read about the new features, or download the release today Spark News Archive
Quick Start - Spark 4. 0. 0 Documentation Where to Go from Here This tutorial provides a quick introduction to using Spark We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write applications in Java, Scala, and Python To follow along with this guide, first, download a packaged release of Spark from the Spark website
Structured Streaming Programming Guide - Spark 4. 0. 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
Downloads - Apache Spark As new Spark releases come out for each development stream, previous ones will be archived, but they are still available at Spark release archives NOTE: Previous releases of Spark may be affected by security issues