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 ...
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
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.
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.
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 ...
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. This guide shows examples with the following ...