Big Data analytics

Big Data analytics with Hadoop & R

COURSE OBJECTIVE:

Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.

PREREQUISITES:

Programming experience and exposure with Data related work such as Reporting, Data Integration, Database Management etc.


COURSE OULINE:

What is Big Data & Why Hadoop?

  • Big Data Characteristics, Challenges with traditional system

Hadoop Overview &it’s Ecosystem

  • Anatomy of Hadoop Cluster, Installing and Configuring Hadoop

  • Hands-On Exercise

HDFS – Hadoop Distributed File System

  • Name Nodes and Data Nodes

  • Hands-On Exercise

Map Reduce Anatomy

  • How Map Reduce Works?

  • The Mapper & Reducer, Input Formats & Output Formats, Data Type & Customer Writable

Developing MapReduce Program

  • Setting up Eclipse Development Environment, Creating Map Reduce Projects,Debugging and Unit Testing MapReduce Code, Testing with MRUnit

Hive, pig & Mahout

  • Hands-On Exercise

 R and Hadoop Overview

  • Introduction to R tool

  • R and Hadoop Integration

  • Hadoop Streaming using R

  • RHadoop Overview

  • RHive Overview

Analytics Project Methodology

  • Analytics Project Overview

  • Steps Invovled in Aanlytics Project

  • Analytics Techniques and Applications in Business

  • Implications of Big Data on Analytics

Working with RHadoop & RHive

  • Word Count Example

  • Airline Optimization Example

  • Retail Store Example

  • Stocks Example

Business Case Study

  • Big Data Analytics Case Study

  • Problem Identification and Solution Design

  • Data Analysis and Visualization

  • Final Insights and Recommendations

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