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

To follow along with this guide, first, download a packaged release of Spark from the Spark website. Since we won’t be using HDFS, you can download a package for any version of …

If you’d like to build Spark from source, visit Building Spark. Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS), and it should run on any platform that runs a …

Spark docker images are available from Dockerhub under the accounts of both The Apache Software Foundation and Official Images. Note that, these images contain non-ASF software …

Spark Connect is a client-server architecture within Apache Spark that enables remote connectivity to Spark clusters from any application. PySpark provides the client for the Spark …

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, …

Spark provides three locations to configure the system: Spark properties control most application parameters and can be set by using a SparkConf object, or through Java system properties. …

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

Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources …

Spark allows you to perform DataFrame operations with programmatic APIs, write SQL, perform streaming analyses, and do machine learning. Spark saves you from learning multiple …