Big Data analytics

Big Data analytics with Hadoop & R


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.


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


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

No comments yet.

Leave a Reply

Ver peliculas online