Mapreduce frameworkis 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-timeMapreduce 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 isan answer to thequestion"What conceptsof HadoopMapreduce 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 practicallyvia aMapreduce program.

Every lecture in this course is explained in 2 Steps.

Step1: Explanation of a Hadoop component |Step 2 : Practicals-How to implement that component in a MapReduce program.

The overall inclusions and benefitsof this course:

• Complete Hadoop Mapreduce explainedfrom scratch to Real-Time implementation.

• Each and Every Hadoop concept is backed by a HANDS-ONMapreduce 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 waythat even a non technical person can understand.

• Mapreduce codes and Datasetsused in lectures areattached for your convenience.

• Includes a section 'Case Studies' that are asked generally in Hadoop Interviews.