A description of four simple steps to augment an existing analytics infrastructure with big data including a simple end-to-end example of how to benefit from this addition of big data. Covers Hadoop, NoSQL, RDBMS and Streaming technologies like Event Processing

1. Big Data in the Data Center
Jean-Pierre Dijcks
Big Data Product Mangement
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2. Agenda
 Architecting Big Data
 Building Big Data Solutions
 Oracle Big Data Appliance
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3. Architecting Big Data
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4. What Does a Big Data World Look Like?
Truck / Motor Manufacturer
What they collect
Monitors the complete car
Miles Per Gallon, Driving techniques
Location information
How they use it
Better tailored servicing plans
Better targeted marketing
Offer better finance deals or related options
More data for R&D
Sell on to partners
Big Data means…
The ability to predict when components might fail
Create better service plans based on actual usage
Drivers can post their data to manufacturers website
Black box data can help with accidents
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5. What Does a Big Data World Look Like?
Retailer / White Goods
What they collect
Food monitoring by RFID tags –
Fridge monitors food usage and sell-
by dates
How they use it
Automated food ordering
Better targeted marketing due to better data about
how/when customer uses products
Big Data means…
More detailed information about customers
Better store based demand planning
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6. Sample of Big Data Use Cases Today
AUTOMOTIVE
Auto sensors
What is the main difference in this data?
COMMUNICATIONS
Location-based
Retail / CPG
Sentiment analysis
FINANCIAL
SERVICES
EDUCATION &
RESEARCH
reporting advertising Hot products Risk & portfolio analysis Experiment
location, New products sensor analysis
Optimized Marketing
problems
HIGH TECHNOLOGY /
Volume, Velocity, Variety
LIFE MEDIA/
ON-LINE
SERVICES / HEALTH CARE
INDUSTRIAL MFG. SCIENCES ENTERTAINMENT SOCIAL Patient sensors,
Mfg quality Clinical trials Viewers / advertising MEDIA monitoring, EHRs
Warranty analysis Genomics effectiveness People & career Quality of care
Cross Sell matching
Web-site
optimization
OIL & GAS These Characteristics Challenge your
Games TRAVEL &
TRANSPORTATION UTILITIES
LAW
ENFORCEMENT
Drilling Adjust to
exploration
sensor analysis
player
behavior
Existing Architecture Meter
Sensor analysis for
optimal traffic flows
Smart
analysis for
& DEFENSE
Threat analysis -
network social media
In-Game Ads Customer sentiment capacity, monitoring, photo
analysis
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7. Building your big data architecture
Gradually Extending your Existing Architecture for Big Data:
 Step 1: Further Analyze Current Data
 Step 2: Architect for Data Variety and Volume
 Step 3: Architect for Data Velocity
 Step 4: Discover New Patterns
Increase Business Value
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8. Step 0: Data Warehouse Foundation
• Dashboard
High Oracle • Ad-Hoc Query
Density
Data Database
Oracle BI
Enterprise
Edition
Acquire Organize Analyze Decide
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9. Step 1: Deep Analysis of Current Data
• Dashboard
High Oracle • Ad-Hoc Query
Density
Data Database
Oracle BI • Churn
Enterprise • Locality
Edition
Spatial
and Graph
Advanced
Analytics
Acquire Organize Analyze Decide
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10. Step 2: Architect for Volume and Variety
• Dashboard
High Oracle • Ad-Hoc Query
Density Database
Data
Oracle BI • Churn
Enterprise • Locality
Low
Density Edition
Spatial • Relationship
variable
Data and Graph • Comments
Hadoop
Advanced
Aggregate Analytics
Pre-Analyze
Acquire Organize Analyze Decide
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11. Step 3: Architect for Velocity
High Oracle
Density
Data Database
• Dashboard
Oracle BI • Ad-Hoc Query
Hadoop Enterprise
Low Edition
Density Spatial and • Churn
Batch Data Graph • Locality
Aggregate
Advanced
Pre-Analyze
Analytics Model • Relationship
• Comments
Streaming Event Real Time
Data Processing Decisions
• Recommend
• Act
Act
Acquire Organize Analyze Decide
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12. Step 4: Discover New Information
Endeca Information Discovery
High Oracle
Density
Data Database
• Dashboard
Oracle BI • Ad-Hoc Query
Hadoop Enterprise
Low Edition
Density Spatial and • Churn
Batch Data Graph • Locality
Aggregate
Advanced
Pre-Analyze
Analytics Model • Relationship
• Comments
Streaming Event Real Time
Data Processing Decisions
• Recommend
• Act
Act
Organize Analyze Decide • Discover
Acquire
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13. Oracle Engineered Systems
Simplify IT – Simplify Big Data • Dashboard

• Ad-Hoc Query
Oracle
Big Data
Appliance
Oracle
Exadata Oracle
• Churn
• Locality 

Exalytics
• Relationship
• Comments
InfiniBand InfiniBand
• Recommend
• Act 
• Discover

Acquire Organize Analyze Decide
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14. Building Big Data Solutions
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15. Building a Big Data Solution
Customer Retention and Social Media
 Apply classic churn models to the Data Warehouse data
 Add Social Data (Twitter) and Relationships (Facebook)
 Analyze Social Data and Relationships
 Deploy New Retention Policy into the Data Stream
Increase Business Value
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16. Predict Customer Churn
Churn
Find at-risk customers
Predictive models
Widely used
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17. Oracle Advanced Analytics
Oracle Data Mining
12 Algorithms Build
Usage
Extreme performance Data Deploy
Customer A: 0.49
Customer B: 0.25
Score
GUI for Data Scientists
Acquire Organize Analyze Decide
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18. Understand Your Customers
Churn Social
Find interesting data sets Data
Process the data to
represent meaningful
actions
Uncover new relationships
New
Facts
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19. Oracle Big Data Appliance
Big Data Acquisition and Organization
Twitter
Data
Pre-integrated cluster Collect
Customer A:
Facebook Customer B:
Sun Oracle hardware Sessionize
Data
Cloudera CDH Aggregate
Massive scale Internal
Log Data
Acquire Organize
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20. Oracle Big Data Connectors
Customer A:
High performance Customer A: Customer B:
Customer B:
Secure
Cust A: 0.49
Efficient Cust B: 0.25
Acquire Organize
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21. Oracle Advanced Analytics
Oracle R Enterprise
Open source compatible Model
Customer A: D 0.10
Customer B: 0.25
Scales to huge data sets
R B
0.79
Extreme performance 0.49
Cust A: 0.49 A C
Cust B: 0.25
Widely used
Organize Analyze Decide
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22. Real-Time Retention Decisions
Churn Social
Data
React to real-time events
Influence key customers
Adjust in real-time
New New
Policy Facts
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23. Oracle RTD and Oracle NoSQL Database
Enrich
Decisions
Self learning Tweet Events Model D
B
Fast profile lookups
A C
Integrated Instant
Communication
Tweet Offer Ideas
Analyze Decide
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24. Oracle Big Data Appliance
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25. Oracle Big Data Appliance
Get up and Running
Quickly
Improve Performance of
Hadoop
Integrated with Exadata
Lower TCO for Big Data
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26. Price comparison
Oracle Big Data Appliance “Build-Your-Own” – HP hardware and Cloudera
Year 1 Year 2 Year 3 Total Year 1 Year 2 Year 3 Total
Servers and
BDA Cost $450,000 $428,220
switches
Support
$54,000 $54,000 $54,000 Support Cost $136,233 $72,000 $72,000
Cost
On-site Installation &
Installation $14,150 configuration
not included
Total $518,150 $54,000 $54,000 $626,150 Total $564,453 $72,000 $72,000 $708,453
Full details at https://blogs.oracle.com/datawarehousing/entry/price_comparison_for_big_data

27. Big Data Appliance performance comparisons
6x faster than custom 20-node Hadoop 2.5x faster than 30-node Hadoop cluster
cluster for large batch transformation jobs for tagging and parsing text documents
10 10
Time (hours)
Time (hours)
5 5
0 0
Big Data DIY Hadoop Big Data Cloud-based
Appliance Cluster Appliance Hadoop
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