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.

There are more guides shared with other languages such as Quick Start in Programming Guides at the Spark documentation. There are live notebooks where you can try PySpark out without any other step:

There are also basic programming guides covering multiple languages available in the Spark documentation, including these: Spark SQL, DataFrames and Datasets Guide

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

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 frameworks …

PySpark combines Python’s learnability and ease of use with the power of Apache Spark to enable processing and analysis of data at any size for everyone familiar with Python.

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.

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

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 …

DataFrame and Spark SQL share the same execution engine so they can be interchangeably used seamlessly. For example, you can register the DataFrame as a table and run a SQL easily as below: