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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.
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.
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.
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Towards the end of the Course, you will be working on a live project where you will be using PIG to perform Big Data analytics. Here are the few Industry-wise Big Data case studies that you will work on:
Project #1: Analysing Aadhar Card Data
Industry: Government Sector
Data: The data set consists of the following fields: State:This field consists of the state names from all over India City:This field consists of city names in all states Approved:This field consists of the total count of approved cards in numbers Rejected:This field consists of the total count of rejected cards in numbers
Problem Statement: Below are few of the problem statements that we have chosen to work on this data set: 1.Find out the total number of cards approved by states. 2.Find out the total number of cards rejected by states. 3.Find out the total number of cards approved by cities. 4.Find out the total number of cards rejected by cities.
Project #2: Analysis of Afghan War Diaries
Industry: Government Sector
Data: The data was written by soldiers and intelligence officers of the United States Military. To keep it simple, we will analyse only four of the available columns (Type, Category, Region and Attack On) in the data set.
Problem Statement: Below are few of the problem statement that we have chosen to work on this data set: 1.To examine all the events that involve explosive hazards. 2.To examine explosive events that involves Improvised Explosive Devices (IEDs).
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