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.
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Future of Data - Big Data
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
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
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)