Big Data
Changing the Way You Manage and Analyze Big Data
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Jean-Pierre Dijcks
Big Data Product Management
Server Technologies

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Use
Data
12%
Executives who feel they
understand the impact data
will have on their organizations
Produce
Data

From Storing Data to Monetizing Data
IT Budget
*Source : ‘Enterprise Architecture As Strategy: Creating a Foundation for Business Execution’ by J Ross, P. Weill, D. Robertson, HBS Press, 2006
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Storing
Data
Managing
Data
Monetizing
Data
Disparate
Data Marts
Enterprise
Data Warehouse
Big Data
Management System
Strategic
Business
Value of IT
Cost
Center
Profit
Center
100%
84%
92%
145%

The Path to Monetizing Big Data
Analytics 2.0
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Analytics 3.0
Analytics 1.0
• Reporting with limited use of
descriptive analytics
• Limited range of tabular data
• Batch oriented analysis
• Analysis bolted onto limited set
of business processes
• Firms “Competing on Analytics”
• Extended analytics to larger and
less structured datasets
• Emergence of Big Data into the
commercial world
• Recognition of Data Science role
in commercial orgs.
• Platform for monetization
• Deeper analysis & more data
• Faster test-do-learn iterations
• Different types of data & wider
business process coverage
• Analysts focus on discovery and
driving business value
• “Agile” with operational elements
incorporated into design patterns
Adapted from: Tom Davenport material – Harvard Business Review (2010)

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Conceptual View
Actionable
Events
Streaming Engine Data Reservoir Enterprise Data & Reporting
Discovery Lab
Actionable
Metrics
Actionable
Data Sets
Input
Events
Execution
Innovation
Discovery
Output
Data
Structured
Enterprise
Data

De Persgroep
Creating a linked customer analytics system
Benefits
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Objectives
 Maximizing customer value
 Optimizing campaign cost through
Automation and Targeting
Solution
 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)
- Toyota Global Vision
Customer Data
Store
Digital, RDBMS,
External
BDA
Mobile
Web
Subscribers
NG Data
Lily
Customer
Analytics
Phase 1:
 Improved Data Quality
 Single View of all Customers improves
customer management
Social
Business
Objects
Oracle Data
Warehouse
Customer
Analytics & aggegated
data

Benefits
 Save over 35,000 call processing minutes per
day
 Analyze network events 40x faster
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Globacom
Improve Customer Information
Objectives
 Respond to customer queries in as close to real
time as possible
 Understand behavior, improve retention, and
increase cross-selling d services
Solution
 Capture and analyzie >1B CDR’s daily in Oracle Big
Data Appliance
 Integrate resulting data, using Oracle NoSQL Database
into online systems
 Leverage xDR Navigator from partner mCentric to
improve first call resolution rates
BDA
mCentric

US-based Bank
Lowering Costs by Simplifying IT Infrastructure
Oracle Enterprise Manager
• Agile business
model
• All data
• De-normalized
& Partial-normalized
Benefits
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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
• Normalized
• Aggregate data
• EDW
Operational Data Store
Mainframe,
RDBMS, more
BDA Exadata
Oracle Data Integrator
Data Delivery
Master
S1
Master
S2
Master
Sn
SOA/API
CRMS
Other
 Faster access to 6x more data
 Lower costs, simplified architecture and fast
time to value

Enterprise Class Big Data Capabilities
BY INDUSTRY &z LINE OF BUSINESS
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
BIG DATA
APPLICATIONS
DISCOVERY
BUSINESS
ANALYTICS
BUSINESS ANALYTICS
DATA RESERVOIR
BIG DATA
MANAGEMENT
DATA WAREHOUSE
SOURCES

Oracle Big Data Management System
Oracle Big Data SQL
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SOURCES
Oracle Database
Oracle Industry
Models
Oracle Advanced
Analytics
Oracle Spatial & Graph
Cloudera Hadoop
Oracle NoSQL Database
Oracle R Advanced
Analytics for Hadoop
Oracle R Distribution
Big Data Appliance
Oracle Database
Oracle Advanced Security
Oracle Advanced
Analytics
Oracle Spatial & Graph
Oracle Exadata
Oracle Big Data
Connectors
Oracle Data
Integrator B

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Strengths of Both Systems
Tooling maturity
5
4
3
2
1
0
Stringent Non-Functionals
ACID transactions
Security
Variety of data formats
Data sparsity
Straight Through Processing
(STP)
Ingestion rate
Cost effectively store data
ETL simplicity
Hadoop
RDBMS
• Hadoop is good at some
things
• Databases are good at others
• SQL is very important

“The implementation of this Big Data
solution will help CaixaBank remain at
the forefront of innovation in the
financial sector, delivering the best and
most competitive services to our
customers”
– Juan Maria Nin, Chief Executive Officer, CaixaBank
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
13

Oracle Communications Data Model
Reference Architecture
Big Data
Platform
(Hadoop/NoSQL)
Relational
Data Warehouse
(OCDM)
Data Management
Feedback Loop
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Customer
Experience
Operations
Monetization
Analytic Apps
ETL/ELT
Adapters
Real-Time
Adapters
Third
Party
Adapters
Oracle Comms Apps
(BSS/OSS)
Oracle Comms
Ntwk Products (Tekelec
& Acme)
Other Oracle Apps
(CRM, ERP, etc.)
Third Party Sources
Data
Sources
To Other Apps
B

Oracle Big Data SQL – A New Architecture
• Powerful, high-performance SQL on Hadoop
– Full Oracle SQL capabilities on Hadoop
– SQL query processing local to Hadoop nodes
• Simple data integration of Hadoop and Oracle Database
– Single SQL point-of-entry to access all data
– Scalable joins between Hadoop and RDBMS data
• Optimized hardware
– Balanced Configurations
– No bottlenecks
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Oracle Confidential – Internal/Restricted/Highly Restricted 15

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Two Challenges
1. Make Hadoop easily consumable for customers
2. Enable Oracle SQL on All Data
16

Recap: Big Data Appliance Overview
Big Data Appliance X4-2
Sun Oracle X4-2L Servers with per server:
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• 2 * 8 Core Intel Xeon E5 Processors
• 64 GB Memory
• 48TB Disk space
Integrated Software:
• Oracle Linux, Oracle Java VM
• Oracle Big Data SQL*
• Cloudera Distribution of Apache Hadoop – EDH Edition
• Cloudera Manager
• Oracle R Distribution
• Oracle NoSQL Database
17
* Oracle Big Data SQL is separately licensed

Recap: Standard and Modular
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18
 Starter Rack is a fully cabled and
configured for growth with 6 servers
 In-Rack Expansion delivers 6 server
modular expansion block
 Full Rack delivers optimal blend of
capacity and expansion options
 Grow by adding rack – up to 18 racks
without additional switches

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Big Data Appliance
 Engineered Systems Benefits
 Lower TCO than DIY Hadoop Clusters
 Faster Time to Value
 Higher Performance out-of-box
 Lower Management Overhead
 Integrated and Comprehensive Security
 Tight Integration with your Infrastructure

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Engineered Systems Benefits
TCO Data Points:
 18 servers (DL380 vs. X4-2L)
 864TB Raw Storage
 288 Cores
 1152GB Total Memory
 Cloudera Enterprise Subscription
with all options
 Subscription vs. Perpetual
 Equivalent Installation Cost
 Not calculated:
 Soft Cost (people and time to value)
 Data integration licenses
$1,400,000
$1,200,000
$1,000,000
$800,000
$600,000
$400,000
$200,000
$0
Year 1 Year 2 Year 3 Year 4 Year 5
Oracle BDA
HP + Cloudera
Savings
List Price Comparisons
Cumulative Cost and Savings

Engineered Systems Benefits
BDA 3.0 DIY CDH 5.0
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Management Console
Single Command Patching and
Upgrade
Full Stack Patching and
Upgrading
Automatic Cluster Re-
Configuration
Security (AAA) out-of-box
Encryption out-of-box (network
and at-rest)
InfiniBand + Optimizations
Stack Tuning (OS, Java, Hadoop)

What does it mean to engineer a BDA?
 Linux Optimization
 Java Configuration
 Pre-Configured AAA security and Encryption
 Pre-Configured Hadoop Settings
 Ex: HDFS, Memory and MR Slots
 Network Optimizations
 Node Configurations (Roles and Growth)
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Security Differentiators
Oracle Database BDA 2.5 DIY CDH 4.6
User Authentication
Row Level Access Controls
Monitoring and Auditing
Encryption at Rest
Network Encryption
Masking, Redaction etc.
Column Lvl Access Ctrl

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BDA Security Overview
 Authentication through Kerberos
 Authorization through Apache Sentry
 Auditing through Oracle Audit Vault
 Encryption for Data-at-Rest
 Network Encryption

Embrace Innovation and Integrate
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Exadata
+
Oracle Database
Big Data Appliance
+
Hadoop & NoSQL
Unify
Development languages
Security
Administration
Support
Workload management
Lifecycle management
Availability

Oracle Big Data Management System
One fast SQL query, on all your data.
Oracle SQL on Hadoop and beyond, with a Smart Scan service
as in Exadata and the security of Oracle Database
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 26

WEB_LOGS CUSTOMERS
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Big Data SQL
27
SELECT w.sess_id, c.name
FROM web_logs w, customers c
WHERE w.source_country = ‘Brazil’
AND w.cust_id = c.customer_id;
Relevant SQL runs on BDA nodes
10’s of Gigabytes of Data
Only columns and rows needed to
answer query are returned
Big Data SQL
B B B
Hadoop Cluster
Oracle Database

WEB_LOGS CUSTOMERS
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Big Data SQL
28
SELECT w.sess_id, c.name
FROM web_logs w, customers c
WHERE w.source_country = ‘Brazil’
AND w.cust_id = c.customer_id;
Relevant SQL runs on BDA nodes
10’s of Gigabytes of Data
Only columns and rows needed to
answer query are returned
Big Data SQL
B B B
Hadoop Cluster
Oracle Database
SQL Push Down in Big Data SQL
• Hadoop Scans on Unstructured Data
• WHERE Clause Evaluation
• Column Projection
• Bloom Filters for Better Join Performance
• JSON Parsing, Data Mining Model Evaluation

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | 29