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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
- Overview - Spark 4. 0. 1 Documentation
Apache Spark is a unified analytics engine for large-scale data processing It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs 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
- Downloads - Apache Spark
Download Spark: spark-4 0 1-bin-hadoop3 tgz Verify this release using the 4 0 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
- Quick Start - Spark 4. 0. 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
- 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
- PySpark Overview — PySpark 4. 0. 1 documentation - Apache Spark
PySpark Overview # Date: Sep 02, 2025 Version: 4 0 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
- SparkR (R on Spark) - Spark 4. 0. 1 Documentation
SparkR is an R package that provides a light-weight frontend to use Apache Spark from R In Spark 4 0 1, SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc (similar to R data frames, dplyr) but on large datasets SparkR also supports distributed machine learning using MLlib
- Spark Release 4. 0. 0 - Apache Spark
Spark Release 4 0 0 Apache Spark 4 0 0 marks a significant milestone as the inaugural release in the 4 x series, embodying the collective effort of the vibrant open-source community This release is a testament to tremendous collaboration, resolving over 5100 tickets with contributions from more than 390 individuals Spark Connect continues its rapid advancement, delivering substantial
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