MapReduce is a programming paradigm that runs in the background of Hadoop to provide scalability and easy data-processing solutions. This course explains the features of MapReduce and how it works to analyze Big Data.

MapReduce is a programming model for writing applications that can process Big Data in parallel on multiple nodes. MapReduce provides analytical capabilities for analyzing huge volumes of complex data.

The MapReduce algorithm contains two important tasks, namely Map and Reduce.

• The Map task takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key-value pairs).

• The Reduce task takes the output from the Map as an input and combines those data tuples (key-value pairs) into a smaller set of tuples.

  The reduce task is always performed after the map job.

This course has been prepared for professionals aspiring to learn the basics of Big Data Analytics using the Hadoop Framework and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.

It is expected that the learners of this course have a good understanding of the basics of Core Java and that they have prior exposure to any of the Linux operating system flavors.