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- 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
- 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
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