Apache Spark Structured APIs | DataFrame, Dataset | PySpark Tutorial

In this lecture, we're going to understand Apache Spark structured api where we will discuss the motive of adding structure to Apache Spark and their benefits. Also we have seen Spark datatypes, schema definition and some high level PySpark structured APIs like DataFrames and Datasets.

Anaconda Distributions Installation link:

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Apache Spark Installation links:

Environment Variables:

HADOOP_HOME- C:\hadoop
JAVA_HOME- C:\java\jdk
SPARK_HOME- C:\spark\spark-3.3.1-bin-hadoop2
PYTHONPATH- %SPARK_HOME%\python;%SPARK_HOME%\python\lib\py4j-0.10.9-src;%PYTHONPATH%

Required Paths:

%SPARK_HOME%\bin
%HADOOP_HOME%\bin
%JAVA_HOME%\bin

Also check out our full Apache Hadoop course:

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Apache Spark Installation links:

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Also check out similar informative videos in the field of cloud computing:

Audience

This tutorial has been prepared for professionals/students aspiring to learn deep knowledge of Big Data Analytics using Apache Spark and become a Spark Developer and Data Engineer roles. In addition, it would be useful for Analytics Professionals and ETL developers as well.

Prerequisites

Before proceeding with this full course, it is good to have prior exposure to Python programming, database concepts, and any of the Linux operating system flavors.

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#pyspark #bigdata #datascience #dataanalytics #datascientist #spark #dataengineering #apachespark

[jankipatel]

Your videos are very informative. Please carry on.

[sohamchatterjee]

Hey! I started following this course a few weeks ago. The content is very informative but it would be very helpful if you could improve the presentations according to what you are telling in the video. It becomes very hard to follow and catch up if you just keep on saying things without presenting them on the screen. Thanks!

[AswiniSaicharitha]

Can i get the code which is used in this videos