
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
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 …
Downloads - Apache Spark
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 …
Quick Start - Spark 4.0.1 Documentation
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 …
PySpark Overview — PySpark 4.0.1 documentation - Apache Spark
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 …
SparkR (R on Spark) - Spark 4.0.1 Documentation
To use Arrow when executing these, users need to set the Spark configuration ‘spark.sql.execution.arrow.sparkr.enabled’ to ‘true’ first. This is disabled by default.
Spark Release 4.0.0 - Apache Spark
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.
Spark Streaming - Spark 4.0.1 Documentation - Apache 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 Release 3.5.5 - Apache Spark
Dependency changes While being a maintenance release we did still upgrade some dependencies in this release they are: [SPARK-50886]: Upgrade Avro to 1.11.4 You can …
Configuration - Spark 4.0.1 Documentation
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. …