Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce

Name: Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce
Rating: 4.4 (510 reviews)

Learn Hadoop Mapreduce from Scratch to Real-time Implementation using Hands-On Mapreduce examples.

Created byJ Garg - Real Time Learning

Last updated 6/2025

English

What you'll learn

Every concept that comes under Hadoop Mapreduce framework from SCRATCH to LIVE PROJECT Implementation.
Learn to write Mapreduce Codes in a Real-Time working environment.
Understand the working of each and every component of Hadoop Mapreduce with Hands-On examples.
Override the default implementation of Mapreduce 's Java classes and code it according to custom requirements.
ADVANCE level Mapreduce concepts which are not easily available online.
Real-time Mapreduce Case studies asked in Hadoop Interviews with its proper Mapreduce code run on cluster.

Course content

16 sections • 53 lectures • 5h 55m total length

Introduction to Mapreduce3:11
In this first lecture of this course Introduction is given to Hadoop closest processing framework i.e. Mapreduce .
Traditional approach VS Hadoop approach2:26
This video explains the difference between the traditional approach and Hadoop approach to do parallel processing of big data. It shows how Hadoop handles most of the tasks by itself.
Basic Flow of a Mapreduce program4:41
In this lecture, Basic flow of Hadoop Mapreduce program is explained.
Mapreduce Program flow with Example4:47
Continuing with the above lecture, this video explains the basic flow of Hadoop Mapreduce program with an example.
Types of File Input formats in Mapreduce6:34
This video explains the basic file input format types of Hadoop Mapreduce. There are mainly 6 Fileinput formats by default provided by Hadoop. We can directly implement them in a Mapreduce program.

Mapper Class structure6:51
This video contains a lecture explaining the default structure of a Mapper class in a Hadoop Mapreduce program.
Reducer Class structure3:23
This video contains a lecture explaining the default structure of a Reducer class in a Hadoop Mapreduce program.
Driver Class structure5:55
This video contains a lecture explaining the default structure of a Driver class in a Hadoop Mapreduce program.
Partitioner Class structure3:48
This video contains a lecture explaining the default structure of a Partitioner class in a Hadoop Mapreduce program. By default, Hadoop implements Hash Partitioner class in MapReduce program.
Shuffling, Sorting & Partitioning in Detail3:04
This is a detailed lecture on How shuffling, sorting and partitioning is done internally in Hadoop architecture. Hadoop does all these 3 steps by itself.
Hadoop Installation8:17
A step by step installation guide(Pdf) to install Hadoop and Mapreduce on your system.

What are Writables in Hadoop5:25
A last lecture before practicals, it explains what type of datatypes Hadoop uses.Also how to use those Hadoop datatypes in a Mapreduce program.
Word Count program in Mapreduce10:31
This lecture consists of an explanation to the basic wordcount program in Mapreduce.
Word count program Code run9:11
After knowing the HadoopMapreduce code of word count, in this lecture it is shown how to actually write that Mapreduce code in eclipse and how to create a jar file out of it and finally how to run it on Hadoop cluster.
What is Combiner in Hadoop Mapreduce7:13
This lecture explains an optimization technique in Hadoop i.e. Combiner. What is combiner in Hadoop, at what phase of a Mapreduce program flow it is used and how it works.
Implementing Combiner in WordCount Mapreduce program3:44
As explained in theory, Combiner in Hadoop can give us better optimization. So in this video we will learn How to implement a Combiner class in a Mapreduce program.

Calculate Sum of Even Odd numbers8:29
This lecture explains how to take out sum of even and odd numbers using a Hadoop Mapreduce program.
Calculate success rate of Facebook ads14:08
Using Mapreduce program, In this video we will calculate the average of success rate of facebook ads of different categories city wise.
Writables - Create our own datatype in Mapreduce7:25
Hadoop provides us with predefined datatypes but it also gives us freedom to create our own datatypes in form of writables.
Thi video explains - How to create our own Hadoop recognized datatypes using a Mapreduce program
Fraud customers of an Ecommerce website - part 18:59
One more example of implementing Hadoop Writables by mapreduce.
In this lecture we will calculate the fraud customers of a ecommerce website. Full Mapreduce codes are attached in resources tab.
Fraud customers of an Ecommerce website - part 27:25
One more example of implementing Hadoop Writables by mapreduce.

In this lecture we will calculate the fraud customers of a ecommerce website. Full Mapreduce codes are attached in resources tab.
Assignment 1

What is Distributed Cache and it's uses in Mapreduce framework4:01
This lecture explains theory of Distributed cache in Hadoop. What is Distributed Cache in Hadoop, what are its uses etc.
Using Distributed cache calculate average salary11:54
Using the knowledge of Distributed Cache in Hadoop, in this video we will implement it in a Mapreduce program
All the Mapreduce codes used in video are attached.

Conceptual code flow of Joins in Mapreduce5:16
A lecture to show the pseudo code flow of joins of a Mapreduce program. It explains the thinking process to do joins in Mapreduce.
Join 2 files in a Mapreduce program9:11
This video shows How to join 2 files in a Hadoop Mapreduce program.
Performing Outer Join in Mapreduce9:04
After performing Inner join in Mapreduce , In this lecture we will perform Outer join in mapreduce .
What is Map Join and Where it is Used5:29
What is Map Join and in what scenarios it should be used in Mapreduce .
Implementing Map Join in a Mapreduce program5:58
After knowing Map Join theory, We will implement it in Mapreduce program with example

What are Counters in Hadoop6:31
What are counters in Hadoop. What purpose so they serve in Hadoop architecture. What are various types of counters supported by Hadoop.
Job Counters5:42
What are Job counters in Hadoop. How to check the counter status after a Mapreduce program run.
Create our own Custom Counters in Mapreduce program10:53
We can custom counters according to out requirements. There are 2 type of counters possible in Hadoop MApreduce framework
Static counters
Dynamic counters
In this lecture we will create a custom counters to calculate number of records processed based on a condition in a store's sales file . Mapreduce codes attached.
Assignment 2

File Input format Class's default structure in Mapreduce7:30
What is file input format class in Hadoop Mapreduce. What are the methods present in it. What methods of it should be overridden in Mapreduce program to create own input formatter
Custom Input Formatter Need & Problem statement7:34
This lecture contains a explanation to why and when there is a need to create a custom input format class in Mapreduce. Hadoop provide us option to create our own input format class to read the input file.
Create custom Input Format class to read XML file | Part 19:29
In this lecture we will create a custom input format class to read a XML file. There are 4 mapreduce Java classes used for this. A proper run on Hadoop cluster is also shown.
Create custom Input Format class to read XML file | Part 212:17
In this lecture we will create a custom input format class to read a XML file. There are 4 mapreduce Java classes used for this. A proper run on Hadoop cluster is also shown.
Create custom Input Format class to read XML file | Part 34:33
In this lecture we will create a custom input format class to read a XML file. There are 4 mapreduce Java classes used for this. A proper run on Hadoop cluster is also shown.
Quiz 2

Requirements

Basic knowledge of HDFS.
Basic knowledge of Core Java.
Rest everything about Hadoop Mapreduce is covered in this course with practicals.

Description

Mapreduce framework is the closest to Hadoop in terms of processing Big data. It is considered as atomic processing unit in Hadoop and that is why it is never going to be obsolete.

Knowing only basics of MapReduce (Mapper, Reducer etc) is not sufficient to work in any Real-time Hadoop Mapreduce project of companies. These basics are just tip of the iceberg in Mapreduce programming. Real-time Mapreduce is way more than that. In Live Big data projects we have to override lot many default implementations of Mapreduce framework to make them work according to our requirements.

This course is an answer to the question "What concepts of Hadoop Mapreduce are used in Live Big data projects and How to implement them in a program ?" To answer this, every Mapreduce concept in the course is explained practically via a Mapreduce program.

Every lecture in this course is explained in 2 Steps.

Step 1 : Theory - Explanation of a Hadoop component

Step 2 : Practicals - How to implement that component in a MapReduce program.

The overall inclusions and benefits of this course:

Complete Hadoop Mapreduce explained from scratch to Real-Time implementation.
Each and Every Hadoop concept is backed by a HANDS-ON Mapreduce code.
Advance level Mapreduce concepts which are even not available on Internet.
For non Java backgrounder's help, All Mapreduce Java codes are explained line by line in such a way that even a non technical person can understand.
Mapreduce codes and Datasets used in lectures are attached for your convenience.
Includes multiple sections on 'Case Studies' that are asked generally in Hadoop Interviews.

Who this course is for:

Students who want to learn Hadoop Mapreduce from SCRATCH to its Live Project Implementation.
Techies who have only basic knowledge of Mapreduce and looking for in-depth knowledge to work in Real-time projects.
Data engineers

Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce

What you'll learn

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Course content

Introduction5 lectures • 22min

Default structure of various classes in Mapreduce6 lectures • 31min

Word Count program in Mapreduce5 lectures • 36min

Mapreduce programs - Examples5 lectures • 46min

Distributed Cache Implementation2 lectures • 16min

Dealing with Input Split Class2 lectures • 7min

Multiple Inputs & Output class2 lectures • 17min

Joins in Mapreduce5 lectures • 35min

Counters in Mapreduce3 lectures • 23min

Creating Custom Input Formatter5 lectures • 41min

Requirements

Description

Who this course is for: