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Development Programming Languages MapReduce

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

A to Z of Hadoop Mapreduce - From Scratch to Real Time Implementation of Mapreduce by HANDS-ON Coding of every component
Rating: 4.2 out of 54.2 (406 ratings)
3,627 students
Created by J Garg - Hadoop Real Time Learning
Last updated 12/2020
English
30-Day Money-Back Guarantee

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 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.
Curated for the Udemy for Business collection

Course content

16 sections • 54 lectures • 5h 55m total length

  • Preview03:11
  • Announcement
    01:17
  • Preview02:26
  • Basic Flow of a Mapreduce program
    04:41
  • Mapreduce Program flow with Example
    04:48
  • Types of File Input formats in Mapreduce
    06:57

  • Mapper Class structure
    06:51
  • Reducer Class structure
    03:23
  • Driver Class structure
    05:55
  • Partitioner Class structure
    03:48
  • Shuffling, Sorting & Partitioning in Detail
    03:04
  • Hadoop Installation
    08:17

  • What are Writables in Hadoop
    05:25
  • Word Count program in Mapreduce
    10:31
  • Word count program Code run
    09:11
  • What is Combiner in Hadoop Mapreduce
    07:13
  • Implementing Combiner in WordCount Mapreduce program
    03:44

  • Calculate Sum of Even Odd numbers
    08:29
  • Calculate success rate of Facebook ads
    14:08
  • Preview07:25
  • Fraud customers of an Ecommerce website - part 1
    08:59
  • Fraud customers of an Ecommerce website - part 2
    07:25
  • Assignment 1
    1 question

  • What is Distributed Cache and it's uses in Mapreduce framework
    04:01
  • Using Distributed cache calculate average salary
    11:54

  • What are Input splits in Hadoop
    05:54
  • Input split Class in Mapreduce
    01:45

  • Multiple Inputs class and its Implementation
    08:49
  • Multiple Output class and its Implementation
    08:32
  • Quiz 1
    5 questions

  • Pseudo code flow of Joins Mapreduce program
    05:16
  • Join 2 files in a Mapreduce program
    09:11
  • Performing Outer Join in Mapreduce
    09:04
  • What is Map Join and Where it is Used
    05:29
  • Implementing Map Join in a Mapreduce program
    05:58

  • What are Counters in Hadoop
    06:31
  • Job Counters
    05:42
  • Preview10:53
  • Assignment 2
    1 question

  • File Input format Class's default structure in Mapreduce
    07:30
  • Custom Input Formatter Need & Problem statement
    07:34
  • Create custom Input Format class to read XML file | Part 1
    09:29
  • Create custom Input Format class to read XML file | Part 2
    12:17
  • Create custom Input Format class to read XML file | Part 3
    04:33
  • Quiz 2
    5 questions

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 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 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 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 : 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.

Who this course is for:

  • Students who want to learn Hadoop Mapreduce from SCRATCH to its Live Project Implementation.
  • Techies who have only basic theoretical knowledge of Mapreduce and need In-depth knowledge of it to work in Real-time projects.
  • Techies who have fear of Java should take this course as the Java Mapreduce codes are explained in very simple and easy manner.
  • Engineers who want to switch their career to Hadoop.

Instructor

J Garg - Hadoop Real Time Learning
Hadoop Trainer
J Garg - Hadoop Real Time Learning
  • 4.3 Instructor Rating
  • 2,016 Reviews
  • 13,564 Students
  • 4 Courses

At Hadoop Real time Learning, the courses are made keeping in mind the Real-time implementation of Big data technologies in Live Projects. We make courses which majorly consist of Hands-On & Practicals. All our courses contain a detailed knowledge of a technology from Scratch to Advance level. Course's lectures explain the codes in such a way that even a Non-technical person can understand.

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