Future of Data : Big Data

Shankar Radhakrishnan

Cognizant

Topics

How did we get here ?

Data Explosion

Big Data

Big Data in an Enterprise

Big Data Platform - Hadoop

Big Data Adoption

Q & A

How did we get here?

Familiar World EDW

Datamarts

Familiar Problems

New World Newer type of data to integrate

Increase in volume

Newer analytical requirements

Data warehouse

Data Integration Problems

Data Processing Problems

Storage Management

Performance Problems

Limitations out of Complexity

Data Explosion

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

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

Big Data

Big Data

Volume

Velocity

Variety

Complexity

“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

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

Big Data in an Enterprise

Data warehouse Data

Sources ETL

Big Data Sources

ETL Big Data Platform

ETL Datamarts

Datamarts

Datamarts Analytical

Applications

Hadoop - Ecosystem

Big Data : Adoption Drivers

Platform

Cluster

Storage

Availability

Process

Distributed

Scalable

Performance

Possibilities

Data Integration

Data Processing

Actionable Insights

Ecosystem

Augmented

TCO

ROI

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)

Q & A