Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce
- 4.4
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Brief Introduction
A to Z of Hadoop Mapreduce - From Scratch to Real Time Implementation of Mapreduce by HANDS-ON Coding of every componentDescription
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.
Requirements
- Requirements
- Basic knowledge of HDFS.
- Basic knowledge of Core Java.
- Rest everything about Hadoop Mapreduce is covered in this course with Practicals.
J Garg - Hadoop Real Time Learning
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