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MapReduce Training

This web-based training course on MapReduce Training functionality, administration and development, is available online to all individuals, institutions, corporates and enterprises in India (New Delhi NCR, Bangalore, Chennai, Kolkatta), US, UK, Canada, Australia, Singapore, United Arab Emirates (UAE), China and South Africa. No matter where you are located, you can enroll for any training with us - because all our training sessions are delivered online by live instructors using interactive, intensive learning methods.

MapReduce is a programming model which works underneath the Hadoop platform while providing scalability and data-processing solutions for Big Data analytics and implementation. Using MapReduce, big data can be processed parallel on multiple nodes providing immense analytical capabilities for analyzing large volumes of complex data. MapReduce inherently takes a task, divides it into various small parts and assigns them to computers. Further, the collected are results are correlated and integrated to form an integrated results dataset. The MapReduce algorithm works by using two important functions Map() and Reduce(). Map() performs the functions of taking data sets and converting them into further data sets broken down into key-value pairs. The Reduce() function further uses the output from the Map as an input and compines the key-value pairs into lesser number of key-value pairs.

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Course Details

This Map Reduce online training course is structured to provide the requisite knowledge of implementing MapReduce as an underlying algorithmic base of Hadoop and leverage its features and capabilities to create effective data analytics models and programs. The course will provide information about working with the sources and sinks of the MapReduce architecture and the ways of working with Tuples (key value pairs) to implement structured and divided data manipulation and processing. This MapReduce Hadoop Big data training program provides information of working with classes, inputformat(), outputformat() and the other different other elements required for creating data models. Integration with Hadoop and Hbase will also be dealt with in this training program. To successfully complete this course it is advised that the trainees have a basic knowledge of core java, linux and data modeling.

MapReduce - Introduction

  • Why MapReduce?
  • How MapReduce Works?
  • The Map task
  • The Reduce task
  • Input Phase
  • Map
  • Intermediate Keys
  • Combiner
  • Shuffle and Sort
  • Reducer
  • Output Phase

MapReduce - Algorithm

  • Mapper Class
  • Reducer Class
  • Tokenize
  • Filter
  • Count
  • Aggregate Counters


  • the Context class
  • RawComparator class


  • The Map phase
  • The combiner phase
  • Reducer phase


  • TF-IDF
  • Term Frequency (TF)
  • Inverse Document Frequency (IDF)

MapReduce - Installation

  • Verifying JAVA Installation
  • Installing Java
  • Verifying Hadoop Installation

MapReduce - API

  • JobContext Interface
  • Job Class
  • Constructors
  • Methods
  • Mapper Class
  • Method
  • Reducer Class
  • Shuffle
  • Sort
  • Reduce

MapReduce - Hadoop Implementation

  • Inputs and Outputs
  • MapReduce Implementation
  • Input Data
  • Compilation and Execution of ProcessUnits Program

MapReduce - Partitioner

  • Partitioner
  • MapReduce Partitioner Implementation
  • Input Data
  • Map Tasks
  • Partitioner Task
  • Reduce Tasks
  • Compilation and Execution

MapReduce - Combiners

  • How Combiner Works?
  • MapReduce Combiner Implementation
  • Record Reader
  • Map Phase
  • Combiner Phase
  • Reducer Phase
  • Record Writer

MapReduce - Hadoop Administration

  • HDFS Monitoring
  • MapReduce Job Monitoring

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