How to Install PySpark on Windows 11 | PySpark Tutorial

Description:
In this comprehensive tutorial, we'll walk you through the step-by-step process of installing PySpark on Windows 11. PySpark is a powerful tool for big data processing that combines the simplicity of Python with the speed of Apache Spark. Whether you're a data scientist, analyst, or developer, getting PySpark up and running on your Windows 11 machine is essential for harnessing the power of distributed computing.

In this video, we'll cover the following topics:

Introduction to PySpark and its importance in data analytics.
Prerequisites for installing PySpark on Windows 11.
Downloading and installing Java Development Kit (JDK).
Setting up Apache Spark on your Windows 11 system.
Configuring environment variables for PySpark.
Installing Python and PySpark packages.
Running your first PySpark script.
By the end of this tutorial, you'll have a fully functional PySpark environment on your Windows 11 computer, ready to tackle big data projects with ease.

Timestamps:
00:00 - Introduction to PySpark
01:12 - Prerequisites
02:30 - Downloading and Installing JDK
04:15 - Setting up Apache Spark
06:20 - Configuring Environment Variables
08:05 - Installing Python and PySpark Packages
18:4 - Running Your First PySpark Script

Don't forget to like, share, and subscribe for more tutorials on data science, machine learning, and programming!

1)download winutils:-
https://github.com/cdarlint/winutils
2)download spark:-
https://spark.apache.org/downloads.html

3) jdk
https://download.oracle.com/java/21/l... (sha256)


ENV Virable:-
HADOOP_HOME - C:\Pyspark\hadoop
JAVA_HOME - C:\Program Files\Java\jdk-21
SPARK_HOME - C:\Pyspark\spark\spark-3.5.0
PYSPARK_PYTHON - C:\Users\LENOVO\AppData\Local\Microsoft\WindowsApps\python.exe
PYTHONPATH - %SPARK_HOME%\python;%SPARK_HOME%\python\lib\py4j-0.10.9.7-src.zip;%PYTHONPATH%


import pyspark from pyspark.sql import SparkSession spark = SparkSession.builder.appName('pyspark-by-examples').getOrCreate() arrayArrayData = [ ("James",[["Java","Scala","C++"],["Spark","Java"]]), ("Michael",[["Spark","Java","C++"],["Spark","Java"]]), ("Robert",[["CSharp","VB"],["Spark","Python"]]) ] df = spark.createDataFrame(data=arrayArrayData, schema = ['name','subjects']) df.printSchema() df.show(truncate=False)