Spark DataFrame Explained | What is DataFrame and how to create it? | Hadoop Tutorial

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This lecture is all about understanding the core concepts of Apache Spark where we have seen what is DataFrame, why we use it, what is the performance improvements while using DataFrame, RDD vs DataFrame how to create DataFrame by different ways in details.

In the previous lecture we have seen understanding the core concepts of Apache Spark where we have seen what is RDD (Resilient Distributed Dataset), how to create an RDD, what are the different actions and transformations which can be applied to RDD in details.

RDD Transformations:
flatMap
map
reduceByKey
sortByKey
filter

RDD Actions:
count()
first()
max()
take()
collect()
saveAsTextFile()

Also we have see where does Spark sits in Hadoop stack and some basic features of Apache Spark.

Get the required files:

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Installing mrjob on HDP 2.6.5 (be sure to "su root" first, as shown in the video.)

yum install python-pip
pip install pathlib
pip install mrjob==0.7.4
pip install PyYAML==5.4.1
yum install nano

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HDP Sandbox Installation links:

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Audience

This tutorial is made for professionals who are willing to learn the basics of Big Data Analytics using Hadoop Ecosystem and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.

Prerequisites

Before you start proceeding with this course, I am assuming that you have some basic knowledge to Core Java, database concepts, and any of the Linux operating system flavors.

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