Overview - Spark 3.5.3 Documentation - Apache Spark
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 processing, and Structured Streaming for incremental computation and stream processing.
Spark Core — PySpark 3.5.3 documentation - Apache Spark
Main entry point for Spark functionality. A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Broadcast ( [sc, value, pickle_registry, …]) A broadcast variable created with SparkContext.broadcast(). A shared variable that can be accumulated, i.e., has a commutative and associative “add” operation.
Documentation - Apache Spark
Training Materials. Training materials and exercises from Spark Summit 2014 are available online. These include videos and slides of talks as well as exercises you can run on your laptop. Topics include Spark core, tuning and debugging, Spark SQL, Spark Streaming, GraphX and MLlib.
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. Download Libraries
Quick Start - Spark 3.5.3 Documentation - Apache Spark
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, first, download a packaged release of …
PySpark Overview — PySpark 3.5.3 documentation - Apache Spark
Spark Core is the underlying general execution engine for the Spark platform that all other functionality is built on top of. It provides RDDs (Resilient Distributed Datasets) and in-memory computing capabilities.
Downloads - Apache Spark
Download Spark: spark-3.5.3-bin-hadoop3.tgz. Verify this release using the 3.5.3 signatures, checksums and project release KEYS by following these procedures. Note that Spark 3 is pre-built with Scala 2.12 in general and Spark 3.2+ provides …
Examples - Apache Spark
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.
Configuration - Spark 3.5.3 Documentation - Apache Spark
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. Environment variables can be used to set per-machine settings, such as the IP address, through the conf/spark-env.sh script on each node.
Migration Guide: Spark Core - Spark 3.5.3 Documentation
Upgrading from Core 3.2 to 3.3. Since Spark 3.3, Spark migrates its log4j dependency from 1.x to 2.x because log4j 1.x has reached end of life and is no longer supported by the community. Vulnerabilities reported after August 2015 against log4j 1.x were not checked and will not be fixed.