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

A to Z of Hadoop MAPREDUCE - From Scratch to its Real Time Implementation with HANDS-ON Coding of every component of MR
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 Practicals.
Override the default implementation of Java classes in Mapreduce and Code it according to our requirements.
ADVANCE level Mapreduce concepts which are even not available on Internet.
Real-time Mapreduce Case studies asked in Hadoop Interviews with its proper Mapreduce code run on cluster.

Mapreduce framework is closest to Hadoop in terms of processing 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 at all 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 Hadoop Mapreduce projects we have to override lot many default implementations of Mapreduce framework to make them work according to our requirements.

This course is answer to the question “What concepts of Hadoop Mapreduce are used in Live 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 : 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 a section ‘Case Studies‘ that are asked generally in Hadoop Interviews.

Introduction

1
Introduction to Mapreduce

In this first lecture of this course Introduction is given to Hadoop closest processing framework i.e. Mapreduce .

2
Announcement

Announcement  lecture to rate this Hadoop Mapreduce course.

3
Traditional approach VS Hadoop approach

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.

4
Basic Flow of a Mapreduce program

In this lecture, Basic flow of Hadoop Mapreduce program is explained.

5
Mapreduce Program flow with Example

Continuing with the above lecture, this video explains the basic flow of Hadoop Mapreduce program with an example.

6
Types of File Input formats in Mapreduce

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.

Default structure of various classes in Mapreduce

1
Mapper Class structure

This video contains a lecture explaining the default structure of a Mapper class in a Hadoop Mapreduce program.

2
Reducer Class structure

This video contains a lecture explaining the default structure of a Reducer class in a Hadoop Mapreduce program.

3
Driver Class structure

This video contains a lecture explaining the default structure of a Driver class in a Hadoop Mapreduce program.

4
Partitioner Class structure

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.

5
Shuffling, Sorting & Partitioning in Detail

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.

6
Hadoop Installation

A step by step installation guide(Pdf) to install Hadoop and Mapreduce on your system.

Word Count program in Mapreduce

1
What are Writables in Hadoop

A last lecture before practicals, it explains what type of datatypes Hadoop uses.Also how to use those Hadoop datatypes in a Mapreduce program.

2
Word Count program in Mapreduce

This lecture consists of an explanation to the basic wordcount program in Mapreduce.

3
Word count program Code run

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.

4
What is Combiner in Hadoop Mapreduce

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.

5
Implementing Combiner in WordCount Mapreduce program

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. 

Set of Mapreduce programs

1
Calculate Sum of Even Odd numbers

This lecture explains how to take out sum of even and odd numbers using a Hadoop Mapreduce program.

2
Calculate success rate of Facebook ads

Using Mapreduce program, In this video we will calculate the average of success rate of facebook ads of different categories city wise. 

3
Writables - Create our own datatype in Mapreduce

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

4
Fraud customers of an Ecommerce website - part 1

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.

5
Fraud customers of an Ecommerce website - part 2

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.

6
Assignment 1

Distributed Cache, Input Split, Multiple Inputs class

1
What is Distributed Cache and it's uses in Mapreduce framework

This lecture explains theory of Distributed cache in Hadoop. What is Distributed Cache in Hadoop, what are its uses etc.

2
Using Distributed cache calculate average salary

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.

3
What are Input splits in Hadoop

This lecture will show the default Mapreduce code of an input split class in Hadoop.

4
Input split Class in Mapreduce

This lecture will show the default Mapreduce code of an input split class in Hadoop.

5
Multiple Inputs class and its Implementation

Hadoop also gives us provision to read more than 1 input files at a time so how exactly we do this in Mapreduce program is shown in this lecture.

6
Multiple Output class and its Implementation

Hadoop also gives us provision to create more than 1 Output directory and how exactly we do this in Mapreduce program is shown in this lecture.

7
Recommendation
8
Quiz 1

Quiz 1

Joins in Mapreduce

1
Pseudo code flow of Joins Mapreduce program

A lecture to show the pseudo code flow of joins of a Mapreduce program. It explains the thinking process to do joins in Mapreduce.

2
Join 2 files in a Mapreduce program

This video shows How to join 2 files in a Hadoop Mapreduce program.

3
Performing Outer Join in Mapreduce

After performing Inner join in Mapreduce , In this lecture we will perform Outer join in mapreduce .

4
What is Map Join and Where it is Used

What is Map Join and in what scenarios it should be used in Mapreduce .

5
Implementing Map Join in a Mapreduce program

After knowing Map Join theory, We will implement it in Mapreduce program with example

Counters in Mapreduce

1
What are Counters in Hadoop

What are counters in Hadoop. What purpose so they serve in Hadoop architecture. What are various types of counters supported by Hadoop.

2
Job Counters

What are Job counters in Hadoop. How to check the counter status after a Mapreduce program run.

3
Create our own Custom Counters in Mapreduce program

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.

4
Assignment 2

Creating Custom Input Formatter

1
File Input format Class's default structure in Mapreduce

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

2
Custom Input Formatter Need & Problem statement

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.

3
Create custom Input Format class to read XML file | Part 1

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.

4
Create custom Input Format class to read XML file | Part 2

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.

5
Create custom Input Format class to read XML file | Part 3

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.

6
Quiz 2

Quiz 2

Different Types of Files in Hadoop

1
Text, Sequence, Avro Files
2
RC, ORC, Parquet Files
3
Performance Test results of Various Files
4
Which File Format to choose
5
Sequence File Implementation in MapReduce

Sequence file is one of the Key-value files supported by Hadoop. In this lecture we will learn How to read and store sequence file in Mapreduce.

Chaining in Mapreduce

1
Chain Mapper and its Implementation

Chain multiple mappers in a Mapreduce program

2
How to Chain Multiple MR Programs

After chain Mapper, We will learn How to chain Multiple Mapreduce programs in single execution.

Real-Time Case Studies in Mapreduce

1
Case study 1 - Bank Loyal Customers

Hadoop Mapreduce is widely accepted by financial institutes to process the data . Once such case study is explained in this lecture where a bank is trying to find out the list of it's loyal customers.

The case study is explained by a full fledged Mapreduce program.

2
Case study 2 - Predicting Churn customers | Part 1

In this case study by using Hadoop Mapreduce ,we are predicting the churn customers | Part 1

3
Case study 2 - Predicting Churn customers | Part 2

In this case study by using Hadoop Mapreduce ,we are predicting the churn customers | Part 2

4
Case study 3 - Flight data Analysis | Part 1

Hadoop Mapreduce case study of flight data to find out less utilized flights.

5
Case study 3 - Flight data Analysis | Part 2

Hadoop Mapreduce case study of flight data to find out less utilized flights.

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