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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
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
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 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
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
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
Getting Started — PySpark 4. 0. 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:
MLlib: Main Guide - Spark 4. 0. 0 Documentation Machine Learning Library (MLlib) Guide MLlib is Spark’s machine learning (ML) library Its goal is to make practical machine learning scalable and easy At a high level, it provides tools such as: ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
RDD Programming Guide - Spark 3. 5. 5 Documentation This guide shows each of these features in each of Spark’s supported languages It is easiest to follow along with if you launch Spark’s interactive shell – either bin spark-shell for the Scala shell or bin pyspark for the Python one