3. BIG DATA is not just HADOOP
Understand and navigate
federated big data sources
Federated Discovery and Navigation
Manage & store huge volume
of any data
Hadoop File System
MapReduce
Structure and control data
Data Warehousing
Manage streaming data
Stream Computing
Analyze unstructured data
Text Analytics Engine
Integrate and govern all
data sources
Integration, Data Quality, Security,
Lifecycle Management, MDM

4. Business-Centric Big Data Enables You to Start With a Critical Business Pain and Expand the
Foundation for Future Requirements
Corresponding Tools
/products
 “Big data” isn’t just a technology—it’s a
business strategy for capitalizing on
information resources
 Getting started is crucial
 Success at each entry point is
accelerated by products within the Big
Data platform
 Build the foundation for future
requirements by expanding further
into the big data platform

5. Velocity
Variety
Volume

6. Merging the Traditional and Big Data Approaches
Traditional Approach
Big Data Approach
Structured & Repeatable Analysis
Iterative & Exploratory Analysis
Business Users
Determine what
question to ask
IT
Delivers a platform to
enable creative
discovery
IT
Business
Structures the
data to answer
that question
Explores what questions
could be asked
Monthly sales reports
Profitability analysis
Customer surveys
Brand sentiment
Product strategy
Maximum asset utilization

7. Raw Data
Valuable Data Assets

9. A) Data Refinery Platform
B) Data Discovery Platform
C)Analytical Tools And Techniques
D)Integrated Data Warehouse
E)Distinct Execution Engine
F)Library Of pre-Built analytic functions
G)Interactive Development Tool

10. SQL for structured and MR for
large scale process analytics
Manage relational & non Relational
data in ins& out of Data Warehouse
Iterative analytics with greater
accuracy and effectiveness
Dig deeper for insights
Within budget

11. Data Task
Low-cost storage
Potential Workloads
•
Retains raw data in manner that can provide low TCO-per-terabyte storage costs
and retention
• Requires access in deep storage, but not at same speeds as in a front-line system
Loading
•
Brings data into the system from the source system
Pre-processing/
•
Prepares data for downstream processing by, for example, fetching dimension
prep/cleansing/
data, recording a new incoming batch, or archiving old window batch.
constraint
validation
Transformation
•
Converts one structure of data into another structure. This may require going
from third-normal form in a relational database to a star or snowflake schema,
or from text to a relational database, or from relational technology to a graph,
as with structural transformations.
Reporting
•
Queries historical data such as what happened, where it happened, how much
happened, who did it (e.g., sales of a given product by region)
Analytics (including
•
Performs relationship modeling via declarative SQL (e.g., scoring or basic stats)
•
Performs relationship modeling via procedural MapReduce (e.g., model building
user-driven, inter-
active, or ad-hoc)
or time series)

12. Stable
(structured)
Evolving
(Semi-Structured)
No Schema
(Has Format only)
• Relatively fixed, Infrequent change
• Leverage strength of relational model & SQL
• Fixed and variable of schema, but changes occur too
quickly
• Leverage backend RDBMS, “LATE BINDING” of
structure by queries
• Less relational, No Semantics – stored in native file
formats
• via MapReduce: Interpret the format & pull out
the required data

13. Stable
Evolving
• ERP Data
• Inventory
Recods
• Web logs,
Call record
• Twitter
feeds
No
Schema
• images
• Videos,
Web Pages

14. What Does Machine Data Look Like?
Sources
Order Processing
Middleware
Error
Care IVR
Twitter
6

15. Machine Data Contains Critical Insights
Sources
Customer ID
Order ID
Product ID
Order Processing
Order ID
Customer ID
Middleware
Error
Time Waiting On Hold
Care IVR
Customer ID
Twitter ID
Twitter
Company’s Twitter ID
Customer’s Tweet

16. Machine Data Contains Critical Insights
Sources
Customer ID
Order ID
Product ID
Order Processing
Order ID
Customer ID
Middleware
Error
Time Waiting On Hold
Care IVR
Customer ID
Twitter ID
Twitter
Company’s Twitter ID
Customer’s Tweet

17. Di
Hadoop captures, stores and
transforms images and call
records
Traditional Work flow
Capture, Retention
and
Transformation
Layer
Data Sources
ETL TOOLS
Analytic Results
Call Center
Voice Records
Analysis and Marketing
Automation (Customer
Retention Campaign)
Discovery
Platform
Dimensional Data
Hadoop
Check Images
path and sentiment
analysis with multistructured data
Social and web
data
Integrated DW