Downloads - Apache Spark Download Spark: spark-4 1 1-bin-hadoop3 tgz Verify this release using the 4 1 1 signatures, checksums and project release KEYS by following these procedures Note that Spark 4 is pre-built with Scala 2 13, and support for Scala 2 12 has been officially dropped Spark 3 is pre-built with Scala 2 12 in general and Spark 3 2+ provides additional pre-built distribution with Scala 2 13 Link with
Overview - Spark 4. 1. 1 Documentation Spark Connect is a new client-server architecture introduced in Spark 3 4 that decouples Spark client applications and allows remote connectivity to Spark clusters
Documentation | Apache Spark Hands-On Exercises Hands-on exercises from Spark Summit 2014 These let you install Spark on your laptop and learn basic concepts, Spark SQL, Spark Streaming, GraphX and MLlib Hands-on exercises from Spark Summit 2013 These exercises let you launch a small EC2 cluster, load a dataset, and query it with Spark, Shark, Spark Streaming, and MLlib
PySpark Overview — PySpark 4. 1. 1 documentation - Apache Spark PySpark Overview # Date: Jan 02, 2026 Version: 4 1 1 Useful links: Live Notebook | GitHub | Issues | Examples | Community | Stack Overflow | Dev Mailing List | User Mailing List PySpark is the Python API for Apache Spark It enables you to perform real-time, large-scale data processing in a distributed environment using Python It also provides a PySpark shell for interactively analyzing your
Quick Start - Spark 4. 1. 1 Documentation Quick Start Interactive Analysis with the Spark Shell Basics More on Dataset Operations Caching Self-Contained Applications 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
Spark SQL DataFrames | Apache Spark Spark SQL is Spark's module for working with structured data, either within Spark programs or through standard JDBC and ODBC connectors
Examples - Apache Spark Apache Spark ™ examples This page shows you how to use different Apache Spark APIs with simple examples Spark is a great engine for small and large datasets It can be used with single-node localhost environments, or distributed clusters Spark’s expansive API, excellent performance, and flexibility make it a good option for many analyses
Spark SQL and DataFrames - Spark 4. 1. 1 Documentation Spark SQL, DataFrames and Datasets Guide 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
Quickstart: DataFrame — PySpark 4. 1. 1 documentation - Apache Spark Quickstart: DataFrame # This is a short introduction and quickstart for the PySpark DataFrame API PySpark DataFrames are lazily evaluated They are implemented on top of RDD s When Spark transforms data, it does not immediately compute the transformation but plans how to compute later When actions such as collect() are explicitly called, the computation starts This notebook shows the basic