TOPICS

About The Course
MapReduce is the underlying engine of Hadoop. The self-paced Comprehensive MapReduce course is designed for the learners to understand and implement various frameworks of MapReduce. The topics are explained using dedicated examples.

Course Objectives
After completion of the Comprehensive MapReduce course, you will be able to:
1. Master the concepts of MapReduce framework
2. Learn to write Complex MapReduce programs
3. Program in YARN
4. Program in MapReduce
Who should go for this course?
This course is designed for professionals aspiring to make a career in Big Data Analytics using MapReduce Framework. Software Professionals, Java Developers, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. Other professionals who are looking forward to acquire a good understanding of MapReduce Framework can also opt for this course.
Pre-requisites
Some of the prerequisites for learning MapReduce include hands-on experience in Core Java and good analytical skills to grasp and apply the concepts in MapReduce.
Why learn Comprehensive MapReduce?
Today, when data is mushrooming and coming in heterogeneous forms, there is a growing need for a flexible, adaptable, efficient and cost effective data analytics which will take minimum on-boarding time. Hadoop fits just perfect in this space and MapReduce being the underlying engine for Hadoop needs to be well understood.

1. Hadoop MapReduce Framework – I
Learning Objectives – In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will learn about YARN concepts in MapReduce.

Topics – MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce.
2. Hadoop MapReduce Framework – II
Learning Objectives – In this module, you will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.

Topics – Input Splits in MapReduce, Combiner, Partitioner, Demos on MapReduce.
3. Advance MapReduce
Learning Objectives – In this module, you will learn Advance MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and how to deal with complex MapReduce programs.

Topics – Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format.