Overview - Spark 3.5.3 Documentation - Apache Spark
Quick Start: a quick introduction to the Spark API; start here! RDD Programming Guide: overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables; Spark SQL, Datasets, and DataFrames: processing structured data …
PySpark Overview — PySpark 3.5.3 documentation - Apache Spark
PySpark is the Python API for Apache Spark. It enables you to perform real-time, large-scale data processing in a distributed environment using Python. It also provides a PySpark shell for interactively analyzing your data.
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. This guide shows examples ...
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 Spark from the Spark website. Since we won’t be using HDFS ...
Documentation - Apache Spark
The Spark examples page shows the basic API in Scala, Java and Python. Research Papers. Spark was initially developed as a UC Berkeley research project, and much of the design is documented in papers. The research page lists some of the original motivation and direction.
API Reference — PySpark 3.5.3 documentation - Apache Spark
API Reference¶ This page lists an overview of all public PySpark modules, classes, functions and methods. Pandas API on Spark follows the API specifications of latest pandas release.
Spark SQL and DataFrames - Spark 3.5.3 Documentation - Apache …
Spark SQL, DataFrames and Datasets Guide. Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed.
Spark Core — PySpark 3.5.3 documentation - Apache Spark
Read a ‘new API’ Hadoop InputFormat with arbitrary key and value class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. SparkContext.newAPIHadoopRDD (…[, …])
Spark Structured Streaming - Apache Spark
Unified batch and streaming APIs. Spark Structured Streaming provides the same structured APIs (DataFrames and Datasets) as Spark so that you don’t need to develop on or maintain two different technology stacks for batch and streaming. In addition, unified APIs make it easy to migrate your existing batch Spark jobs to streaming jobs.
ML Pipelines - Spark 3.5.3 Documentation - Apache Spark
MLlib standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow. This section covers the key concepts introduced by the Pipelines API, where the pipeline concept is mostly inspired by the scikit-learn project.