Presented at TOUG on May 21, 2014

1. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.1
Big Data:
Myths & Realities
Oleksiy Razborshchuk
Distinguished Solution Architect
Oracle Canada ULC
May 21st, 2014
People. Process. Portfolio.

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Agenda
 Big Data
 Oracle’s Big Data Solution and Differentiators
 Use Cases and Implementation Examples

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True or False?

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BLOG
What Makes Big Data BIG DATA?
Volume
• Very large
quantities
of data
Velocity
• Extremely
fast streams
of data
Variety
• Wide range
of datatype
characteristics
BLOG
Telematics
Social
Social
Value
• High potential
business value
if harnessed

5. Challenge:
Exploiting
Synergies
Big Data. Big Architecture.
ANALYZE
DECIDE ACQUIRE
ORGANIZE

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Basics Of Hadoop
In Memory
File 1 Piece 1 1
File 1 Piece 2 2
File 1 Piece 3 3
2 5
3 6
4 7
Name Node
Data Node Data Node Data Node Data NodeJAR
Map
Reduce
Map
Reduce
Map
Reduce
Map
Reduce
Job Tracker Task TrackerTask TrackerTask TrackerTask Tracker

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MapReduce Example
Hello World Goodbye World
<K,V>
<Hello,1>
<World,1> <Goodbye,1>
<World,1>
<K,V,V,V,V>
<World,1,1> <Hello,1> <Goodbye,1>
<Goodbye,1><Hello,1><World,2>

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Wrap Up

9. Hadoop Architecture
9

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Cloudera Stack

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Active Archive
Transformation and Processing
Self-Service Exploratory BI
Advanced Analytics
Enterprise Data Hub (EDH)

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What is Big Data Environment?
VS&

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Unified Data Analytics Environment
Unified Analytics API
SQL R MR
Unified Analytics Processing Platform
Hadoop RDBMS
Management Framework and Tools

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Big Data in the Enterprise Information Architecture Strategy
14

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Agenda
 Big Data
 Oracle’s Big Data Solution and Differentiators
 Use Cases and Implementation Examples

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Oracle Big Data Appliance
Better
TCO
Faster Time
to Value
OptimizedLower risk. Engineered to perform.

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What Do We Mean by Commodity DIY ?
Red Hat / CentOS
Different
Platform
Every
Time
Integrated
Tuned
Optimized
Identical
Applications
Compute
& Storage
Networking
OS
CPU, RAM, Blade, Rack
Cisco
120+ separate parts
Months from start to production
1 Big Data Appliance
Unpack to production in days
Hadoop Distribution

18. 18© 2014 Oracle Corporation and CIBC – Proprietary and Confidential
Why Oracle Big Data Appliance vs. Commodity
With proof points on the following slides
• Designed and Engineered by Cloudera & Oracle (OEM)
• Big Data Best Practices already implemented
• Pre-Integrated, pre-optimized, and pre-tuned before arrival
• Comprehensive (all h/w, s/w, tools, integration labour)
• Manageability top-to-bottom
• Secure and hardened
• Shorter deployment and time to market
• Faster Performance
• Lower TCO

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BDA TCO Beats Build Your Own Hadoop Cluster
$0
$200,000
$400,000
$600,000
$800,000
$1,000,000
$1,200,000
$1,400,000
Year 1 Year 2 Year 3 Year 4 Year 5
Oracle BDA
HP+Cloudera
Cisco+Cloudera
Dell+Cloudera
IBM+Cloudera

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Engineered by Cloudera and Oracle
 Managed Distribution
– Components certified to work together and on Oracle Big Data Appliance in regular
updates, on the same hardware/software stack as all our customers
 Cloudera’s Hadoop Knowledge Engineered into the system
– Master service lay-out, settings for Hadoop parameters
– Optimized data block size, number of Map-Reduce slots
– Infiniband fabric optimized
 Enterprise Hadoop Features jointly developed
– Multi-Homing for Hadoop
– Highly Available NameNode Solution
– Tight integration between Oracle Enterprise Manager and Cloudera Manager
– Sentry security (invented by Cloudera and Oracle)

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Engineered for Quicker Time and Lower Cost
http://www.oracle.com/us/corporate/analystreports/industries/esg-big-data-wp-1914112.pdf
ESG believes that a "buy" versus "do-it-yourself"
approach will yield roughly one-third faster time-
to-market benefit improvement...
0
5
10
15
20
25
30
Oracle Big Data Appliance Build it yourself
Time to Market (Weeks)
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
Oracle Big Data Appliance Build it yourself
Cost: Initial Infrastructure/Tasks
[…] nearly 40% cost savings versus IT
architecting, designing, procuring, configuring,
and implementing its own big data infrastructure.
Compared with a DIY Cluster

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Engineered for Performance
Compared with a DIY Cluster
0
5
10
Big Data
Appliance
DIY Hadoop
Cluster
Time(hours)
 Configured for exceptional
performance on delivery
 6x faster than custom 20-node
Hadoop cluster for large batch
transformation jobs
 Engineering done by Oracle and
Cloudera:
– OS and File System Tuning
– Java Virtual Machine Tuning
– Hadoop Configuration and Setup
6x

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Enterprise-Grade Big Data
BDA 2.5 DIY CDH 4.6
Integrated Management Console
Single Command, Full Stack
Patching and Upgrade
Automatic Cluster Re-Configuration
Encryption and Auditing
out-of-box
Authentication, Access Control
HA / DR
Engineered by Cloudera for EDH
Tuned and Optimized Performance
(OS, Java, Hadoop, Infiniband)

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Oracle Unified Information reference architecture
Native integration between BDA and Exadata (like iPhone and iPad)
Stream Acquire – Organize – Analyze
Oracle BI
Foundation Suite
Oracle Real-Time
Decisions
Endeca Information
Discovery
Decide
Oracle Event
Processing
Oracle Big Data
Connectors
Oracle Data
Integrator
Oracle
Advanced
Analytics
Oracle
Database
Oracle OLAP,
Spatial,
Graph
Apache
Flume
Oracle
GoldenGate
Oracle
NoSQL
Database
Cloudera
Hadoop
Oracle R
Distribution
Oracle
Coherence
Oracle Big Data Appliancea Oracle Exadata

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Big Data Connectors and Data Integrator
Big Data Appliance
+
Hadoop
Exadata
+
Oracle Data
Warehouse
15TB/hour
10xFaster

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Agenda
 Big Data
 Oracle’s Big Data Solution and Differentiators
 Use Cases and Implementation Examples

27. 27 Copyright © 2014, Oracle and/or its affiliates. All rights reserved.
Big Data Solutions for Financial Services
IT Optimization
Big Data Analytics
Business Process Transformation
• ETL and batch processing • Extended Data Warehouse
• Mainframe offloading • Active Archiving
• Customer 360 • Omni-channel CX
• Cross-selling / Geo-fencing • Payment Analytics
• AML / Anti-Fraud • Risk Management
• Pricing Management • Compute Offload (VAR)

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Customer 360 with NGData Lily
 Oracle’s Big Data Value
Added Partner
 Individual Customer
Behaviour Translated into
Industry Specific KPIs
Customers include:
Socio-demo
Life Time Events
Mobility
Affluence
Social
Affinity
Lifestyle
Competitor
Segment
Communication Preferences
Communication History
Customer Status
Products
Usage
Customer Engagement
CLTV
Loyalty
Customer Experience
Customer DNA

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Case Study
Lowering Costs by Simplifying IT Infrastructure
Objectives
 Comply with regulations requiring more
data to support stress testing
 Reduce IT costs & streamline processing
by eliminating duplicate data stores
Solution
 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
- Toyota Global Vision
Operational Data Store
Mainframe,
RDBMS, more
BDA Exadata
• Agile business
model
• All data
• De-normalized
& Partial-
normalized
• Normalized
• Aggregate data
• EDW
Oracle Enterprise Manager
Oracle Data Integrator
Data Delivery
Master
S1
Master
S2
Master
Sn
SOA/API
CRMS
Other
 Fast access to 85% more data
 Lower costs, simplified architecture and
fast time to value
Benefits

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3 Key Takeaways from this presentation
• Big Data is not just Hadoop
• Key BD use cases: Active Archive, Data Processing, BI Analytics
• Oracle+Cloudera = most complete & integrated solution in the industry