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.

This page explains the Spark Connect architecture, the benefits of Spark Connect, and how to upgrade to Spark Connect. Let’s start by exploring the architecture of Spark Connect at a high level.

In Apache Spark 3.4, Spark Connect introduced a decoupled client-server architecture that allows remote connectivity to Spark clusters using the DataFrame API and unresolved logical plans as the …

In Apache Spark 3.4, Spark Connect introduced a decoupled client-server architecture that allows remote connectivity to Spark clusters using the DataFrame API and unresolved logical plans as the …

Apache Spark™ Documentation Setup instructions, programming guides, and other documentation are available for each stable version of Spark below: Spark

Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

This document gives a short overview of how Spark runs on clusters, to make it easier to understand the components involved. Read through the application submission guide to learn about launching …

Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only “added” to, such as …

Spark now comes packaged with a self-contained Maven installation to ease building and deployment of Spark from source located under the build/ directory. This script will automatically download and …

Apache Spark DataFrames support a rich set of APIs (select columns, filter, join, aggregate, etc.) that allow you to solve common data analysis problems efficiently.