This document discusses big data and how enterprises are adopting big data solutions. It describes how data has exploded in terms of volume, velocity, and variety. Big data now includes structured, semi-structured, and unstructured data from sources like sensors, social media, and machine logs. The document outlines how Hadoop has become a popular big data platform that provides scalable and cost-effective storage and processing of large, complex datasets. It also discusses how enterprises are using big data for applications like predictive analytics, social intelligence, and mobile analytics to drive insights and decisions.

1. Future of Data : Big Data
Shankar Radhakrishnan
Cognizant

2. Topics
 How did we get here ?
 Data Explosion
 Big Data
 Big Data in an Enterprise
 Big Data Platform - Hadoop
 Big Data Adoption
Q&A

3. How did we get here?
Familiar World
Data Integration Problems
 EDW
 Datamarts Data Processing Problems
 Familiar Problems Data
warehouse
Storage Management
Performance Problems
Limitations out of Complexity
New World
 Newer type of data to integrate
 Increase in volume
 Newer analytical requirements

4. Data Explosion

5. Newer Interests
 Social Intelligence
 DBIM, Sentiment Analysis, Social Customer Care
 Predictive Analytics
 Propensity, Price Elasticity, Anti-Fraud Analytics
 Segmentation Insights
 Funnel Analysis, Behavioral Patterns, Cohort Analysis
 Mobile Analytics
 Ad-Targeting, Geo-spatial Analytics

6. Categories
 Structured Data
 Enterprise Data (CRM, ERP, Data Stores, Reference Data)
 Semi-structured Data
 Machine Generated Data (Sensor Data, RFIDs)
 Unstructured Data
 Social Data (Comments, Tweets), Blog posts

7. Big Data
Volume
Complexity
Big Velocity
Data
Variety
“Big Data” refers to high volume, velocity, variety and complex information assets that
demand cost-effective, innovative forms of information processing for enhanced insight
and decision making

8. Big Data Platforms
• Data Integration
o Informatica, Infosphere
o talenD, Pentaho, Karmasphere, Apache Sqoop, Apache Flume
• Database Framework
o Hadoop (Distributions: Cloudera, Hortonworks, MapR)
o Hbase
o Hive
• NoSQL Databases
o MongoDB, CouchDB
• Machine Data Processing
o Splunk, Mahout
• Text Analytics
o Clarabridge, Lexanalytics

9. Big Data in an Enterprise
Big Data Big Data
ETL
Sources Platform
Datamarts
ETL Analytical
Datamarts Applications
Datamarts
Data
ETL Data warehouse
Sources

10. Hadoop - Ecosystem

11. Big Data : Adoption Drivers
Cluster Distributed
Platform Storage Scalable Process
Availability Performance
Data
Augmented
Integration
Data
Possibilities Processing
TCO Ecosystem
Actionable
ROI
Insights

12. Big Data – Adoption Scenarios
 Replatforming to Big Data (Hadoop, MapR)
 Archival Solution (Hadoop)
 Offloading Data warehouse, EDW (Hadoop, Hive)
 Social Media Integration
 Machine Data Analysis (Splunk, Mahout)
 Complex Analytical Requirements (Hbase)

13. Q&A