After investing years in the data warehouse, are you now supposed to start over? Nope. This session discusses how to leverage Hadoop and big data technologies to augment the data warehouse with new data, new capabilities and new business models.

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2. Expand your Data Warehouse
with new Data Streams
Jean-Pierre Dijcks
Team Lead – Big Data Product Management

3. Agenda
 Big Data Technologies
 Big Data Trends and Patterns
 Next Generation Data Infrastructure
 Big Data Appliance
 Summary
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4. New as well as Proven Technologies
 The Hadoop Family
 The NoSQL Family
 Event Processing Technologies
 Oracle Database / Oracle Exadata
 And more…
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5. Hadoop
Framework for distributed processing
Large Data Sets
Clusters of Computers
Simple Computing Models
Highly Available Service
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6. Cross-Platform Strengths





Extreme Performance
Highly Secure
Analytic SQL
Rich Tool Set
Vast Expertise
 Low-cost Scalability
 Flexible Schema on
Read
 Abstract Storage Model
 Open
 Rapid Evolution
Exadata
6
Big Data Appliance
+
Oracle Database
+
Hadoop
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7. New as well as Proven Technologies
A simple segmentation of usage
Recommendations
NoSQL DB
Profiles
Correlation
Usage Logs
Aggregation
Transactions
Counters
Hadoop
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RDBMS

8. New as well as Proven Technologies
A simple segmentation of usage
Recommendations
NoSQL DB
Profiles
Interaction
Correlation
Usage Logs
Aggregation
Transactions
Counters
Hadoop
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RDBMS

9. Fact: Data Expansion
AUTOMOTIVE
Auto sensors
reporting
location, proble
ms
COMMUNICATIONS
Location-based
advertising
HIGH TECHNOLOGY /
INDUSTRIAL MFG.
Mfg quality
Warranty analysis
TRAVEL &
TRANSPORTATION
Sensor analysis for
optimal traffic flows
Customer sentiment
MEDIA/
ENTERTAINMENT
Viewers / advertising
effectiveness
Cross Sell
FINANCIAL
SERVICES
Risk & portfolio analysis
New products
9
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Retail / CPG
Sentiment analysis
Hot products
Optimized Marketing
OIL & GAS
Drilling
exploration
sensor analysis
LIFE
SCIENCES
Clinical trials
Genomics
Games
Adjust to
player
behavior
In-Game Ads
ON-LINE
SERVICES /
SOCIAL
MEDIA
People & career
matching
Web-site
optimization
UTILITIES
Smart Meter
analysis for
network
capacity,
EDUCATION &
RESEARCH
Experiment
sensor analysis
HEALTH CARE
Patient
sensors, monitoring
, EHRs
Quality of care
LAW
ENFORCEMENT
& DEFENSE
Threat analysis social media
monitoring, photo
analysis

10. Fact: Data Expansion
AUTOMOTIVE
Auto sensors
reporting
location,
problems
COMMUNICATIONS
Location-based
advertising
HIGH TECHNOLOGY /
INDUSTRIAL MFG.
Mfg quality
Warranty analysis
TRAVEL &
TRANSPORTATION
Sensor analysis for
optimal traffic flows
Customer sentiment
MEDIA/
ENTERTAINMENT
Viewers / advertising
effectiveness
Cross Sell
FINANCIAL
SERVICES
Risk & portfolio analysis
New products
10
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Retail / CPG
Sentiment analysis
Hot products
Optimized Marketing
Games
Adjust to
player
behavior
In-Game Ads
Analyze Bigger, More
Diverse Data Sets
OIL & GAS
Drilling
exploration
sensor analysis
ON-LINE
SERVICES /
SOCIAL
MEDIA
People & career
matching
Web-site
optimization
Finding and Monetizing
Unknown Relationships
LIFE
SCIENCES
Clinical trials
Genomics
UTILITIES
Smart Meter
analysis for
network
capacity,
Data Driven
Business Decisions
EDUCATION &
RESEARCH
Experiment
sensor analysis
HEALTH CARE
Patient
sensors, monitoring
, EHRs
Quality of care
LAW
ENFORCEMENT
& DEFENSE
Threat analysis social media
monitoring, photo
analysis

11. Capturing Data Expansion
Expand Data Warehouse with Data Reservoir
•
•
Online
Scalable
Hadoop
• Flexible
• Cost
Effective
11
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Σ
Business
Intelligence
Σ
Data Warehouse
Marts

12. Capturing Data Expansion
Instant Responses to Streaming Data
Event
Decisions
NoSQL
Hadoop
•
•
•
•
12
Online
Scalable
Flexible
Cost
Effective
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Data Warehouse
Business
Intelligence

13. Oracle Big Data Solution
Business Analytics
Oracle Real-Time
Decisions
Endeca Information
Discovery
Oracle Event
Processing
Cloudera
Hadoop
Apache
Flume
Oracle
NoSQL
Database
Oracle
GoldenGate
Fast Data
13
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Oracle R
Distribution
Oracle BI
Foundation Suite
Oracle Big Data
Connectors
Oracle
Database
Oracle
Advanced
Analytics
Oracle Data
Integrator
Big Data Platform
Oracle
Spatial
& Graph

14. Expanding the DW
Architecture
Big Store
Data Warehouse
Data
Hadoop
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RDBMS

15. Expanding the DW
Architecture
Batch processing of data moves to
Hadoop and enables more cycles
for analytics in the DW
1
Data
2
Other diverse streams of data enter the
Hadoop reservoir for processing and
correlation or exploration
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16. Expanding the DW
Architecture
1
Data
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New data sets are continuously
generated and moved for both
mass-comsumption and further
analysis in the DW

17. Expanding the DW for Streaming Data
Logical Architecture
Cache
Data
Data Warehouse
Event
Processor
Big Store
17
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18. Expanding the DW for Streaming Data
Logical Architecture
Hold data for a
short time then flush
Data
EP
NoSQL
Main analytics and
publishing system
RDBMS
Act on data
as it streams
Hadoop
Long term retention
and long running analytics
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19. Expanding the DW for Streaming Data
Logical Architecture
Data is received and kept
for a “short duration” to enable
real-time monitoring and views
2
Data
1
Data is transported and acted
upon while streaming using
event processing logic
Without moving it, data is
3 exposed (sometimes
aggregated) to the real-time
DW
4 Without moving it, data is
exposed to Hadoop processing
enabling an up-to-date view
19
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20. Expanding the DW for Streaming Data
Logical Architecture
Data
As data is flushed, aggregates
1 are materialized in the DW to
avoid double work
Raw data is flushed into the 1
Hadoop store
for long term storage and
analytics on
raw data (plus aggregation)
20
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2
New data sets (aggregations and more)
are continuously created in the DW
for mass-consumption and consolidated
analysis

21. Expanding the DW for Streaming Data
Logical Architecture
Event Processing reads relevant
data with low latency from cache
Business logic (scores, profiles) are
moved into the cache for fast look-up
by the Event Processors
2
Data
3
Decisions and results
are stored with context
in Big Store
21
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1
1
4
Results are continuously evaluated
and scores etc are re-calculated

22. Oracle Big Data Solution
Business Analytics
Oracle Real-Time
Decisions
Endeca Information
Discovery
Oracle Event
Processing
Cloudera
Hadoop
Apache
Flume
Oracle
NoSQL
Database
Oracle
GoldenGate
Fast Data
22
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Oracle R
Distribution
Oracle BI
Foundation Suite
Oracle Big Data
Connectors
Oracle
Database
Oracle
Advanced
Analytics
Oracle Data
Integrator
Big Data Platform
Oracle
Spatial
& Graph

23. Big Data Connectors and Data Integrator
15TB / hour
10x Faster
Big Data Appliance
+
Hadoop
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Exadata
+
Oracle Database
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24. Analyze All Your Data In-Place
Advanced
Analytics
Big Data Appliance
+
Hadoop
24
Exadata
+
Oracle Database
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25. Expose All Data to End Users
OBI EE
Endeca
Big Data Appliance
+
Hadoop
25
Exadata
+
Oracle Database
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.

26. Oracle Big Data Solution
Business Analytics
Oracle Real-Time
Decisions
Oracle Event
Processing
Endeca Information
Discovery
Cloudera
Hadoop
Oracle BI
Foundation Suite
Scalable, low-cost data storage
Oracle
Oracle Big Data
and processing Database
engine
Connectors
Apache
Flume
Oracle
GoldenGate
Fast Data
26
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
Oracle
NoSQL
Database
Oracle R
Distribution
Oracle
Advanced
Analytics
Scalable key-value store
Oracle Data
Integrator
Oracle
Spatial
& Graph
Statistical analysis framework
Big Data Platform

27. Big Data Appliance for Hadoop and NoSQL
Multi-Purpose
Big Data
Platform
Lower TCO
than DIY
Hadoop
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Simplified
Operations
Comprehensive
Security

28. Big Data Appliance
Multi-Purpose Big Data Platform
 Includes all components of Cloudera Enterprise and Add-ons
– Cloudera CDH
– Cloudera Impala
– Cloudera HBase (with Apache Accumulo)
– Cloudera Search
– Cloudera Manager (incl. BDR and Navigator)
 Oracle NoSQL Database
 Oracle R Distribution
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29. Big Data Appliance
Lower TCO than DIY Hadoop
$700k
%
40
Beats a DIY Cluster on:
 Initial Cost
 Time to Value
 Operations
 Performance
Cost Savings
%
33
Faster Time to
Value
$0k
DIY
29
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BDA

30. Big Data Appliance
Simplified Operations
 Out-of-the-Box Performance
 One-command:
– Installation
– Patching
– Updating
– Expansion
 Enterprise Manager Plug-in for Big Data Appliance
 Single Contact for all Support
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31. Big Data Appliance
Comprehensive Security
 Authenticate
 Authorize
 Audit
Hadoop
Non-Relational Data
Audit Vault
Databases
Relational Data
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Operating
Systems

32. Western European Media Company
Creating a linked customer analytics system
Objectives
Benefits
 Maximizing customer value
Phase 1:
 Improved Data Quality
 Single View of all Customers improves
- Toyota Global Vision
customer management
 Optimizing campaign cost through
Automation and Targeting
Solution
Business
Objects
Mobile
 Single, rich customer repository based on
Big Data Appliance and NG Data® Lily®
 Analytics drive:
 subscriber management (up-sell/cross-sell,
churn, conservation)
 editorial use (article engagement, adapt content
over time)
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Subscribers
Web
Social
Digital, RDBMS,
External
BDA
NG Data
Lily
Customer
Analytics
Customer Data
Store
Oracle Data
Warehouse
Customer
Analytics &
Aggregated data

33. US-based Bank
Lowering Costs by Simplifying IT Infrastructure
Objectives
Benefits
 Comply with regulations requiring more
 Fast access to 85% more data
data to support stress testing
 Reduce IT costs & streamline processing
by eliminating duplicate data stores
 Lower costs, simplified architecture and
fast time to value
- Toyota Global Vision
Solution
Oracle Enterprise Manager
S1
Master
 Single, reliable BDA/Exadata-based ODS
supporting all downstream systems
 Landing zone & archival repository for
both structured & unstructured data
 Use Exadata as “19th” BDA node
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• Agile business
model
• All data
• De-normalized
& Partialnormalized
S2
Master
• Normalized
• Aggregate data
• EDW
Sn
Master
BDA
Exadata
Oracle Data Integrator
Mainframe, RD
BMS, more
Operational Data Store
SOA/API
CRMS
Other
Data Delivery

34. Thomson Reuters
Identifying Cross Sell and Upsell Opportunities
Objectives
 Maximize cross-sell opportunities
 Lower cost and complexity
Solution
 “Oracle's engineered systems… are geared
toward high performance big data delivery - and
that is exactly the type of work we do”
Rick King
Chief Operating Officer for Technology
Thomson Reuters
 Economically capture all customer activity
Upsell/Cross Sell
 Testing 50M events/sec ingest rates into
BDA and Oracle NoSQL DB
 Feeds Exadata EDW for customer
profitability & segmentation analysis
Research
Applications
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EDW
Sandbox & DR
BDA
34
Event Capture
& Store
Exadata
Interactive
Analytics
Exalytics

35. Summary
The Future is Now!
 Business requirements are here now
– Your end users want more analytics and more data
 Technology is available
– Hadoop, NoSQL, Event Processing are commercially available
 Technology is increasingly easy to consume
– Oracle Big Data Appliance, Oracle Exadata make deployment much
simpler compared to DIY systems
 Your competitors are doing this today – so should you!
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