1. Enriching Your
Data Warehouse
With Hadoop
Presenting with
Peter Yu, Senior Director
iTech Asia-Pacific & Japan

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Agenda
 Opportunity
 Challenges
 Strategy
 Examples
 Best Practices

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Connecting With Your Customer

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Big Data Improves Operational Performance
Source: Economist Intelligence Unit, .”The Deciding Factor: Big Data and Decision Making“
Big data benefits
seen growing
substantially

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Use
Data
12%
Executives who feel they
understand the impact data
will have on their organizations
Produce
Data
The Problem

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DON’T BELIEVE
EVERYTHING
YOU READ

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Electricity: AC or DC?

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RDBMSRDBMS
Today
Discovery & Analytics
Business Intelligence

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RDBMSRDBMS
Today
Discovery & Analytics
Business Intelligence
External
ETL Cluster

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RDBMSRDBMS
What about Archived Data?
Discovery & Analytics
Business Intelligence
Archive
External
ETL Cluster

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RDBMSRDBMS
What about New Data?
Discovery & Analytics
Business Intelligence
?
Archive
External
ETL Cluster

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RDBMSRDBMS
Expand Your Data Warehouse
Discovery & Analytics
Business Intelligence
?
External
ETL Cluster
Archive

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RDBMS
Hadoop
RDBMS
Hadoop
Integrate Hadoop and RDBMS
Discovery & Analytics
Business Intelligence

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“[Facebook] started in the Hadoop world. We are now
bringing in relational to enhance that. We're kind of going [in]
the other direction ... We've been there, and [we] realized
that using the wrong technology for certain kinds of
problems can be difficult.”
Ken Rubin
Director of Analytics
Facebook
http://tdwi.org/Articles/2013/05/06/Facebooks-Relational-Platform.aspx?Page=1

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Get Fast Answers
to New Questions
Create a Data
Reservoir
Predict More,
More Accurately
Accelerate
Data-Driven Action
Key Hadoop Use Cases
Complementing An Existing Data Warehouse
ETL

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• Transactional data
• Customer information
• Web log and session data
• Machine/Sensor data
• Historical data
Data Reservoir
Keep All Potentially Valuable Data in One Place

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RDBMSRDBMS
Today
Discovery & Analytics
Business Intelligence
?
Archive
External
ETL Cluster

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RDBMS
Hadoop
RDBMS
Hadoop
Create an Active Archive with Hadoop
Discovery & Analytics
Business Intelligence
Σ

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 80 years of historical data in Hadoop
 Structured and unstructured data includes
customer data, economic trends, telematic
sensors, weather, public data
 Integrated with mainframes and EDWs
 Before Hadoop, could analyze only one
state, took 24 hours
 With Cloudera, can analyze risk across all
50 states, in 16 hours (500x improvement)
 First 3 use cases: Data hub, ETL offload,
advanced analytics
Comprehensive risk analysis
Customer Example: Insurer Real-Time Data Hub
Cloudera
Hadoop
EDW and
Mainframe
Customer
Data

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 Optimize offers
 Increase revenue
 Lower costs (offer bytes?)
 Reduce complexity
 Faster time to value
Maximize offer effectiveness
Customer Example: Travel Industry
Big Data
Appliance
Legacy
Data
Warehouse
Customer
Data

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 Fast access to 85% more data
 Lowered costs
 Simplified architecture
 Faster time to value
Compliance, cost reduction
Customer Example: Regional Bank
Big Data
Appliance
Oracle
Exadata
Mainframe,
RDBMS
Oracle Data Integrator

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• Batch window constraints
• Adding value vs. adding cost
• Analysis vs. Transformation
• Analysis vs. Data movement and replication
• Uncertain value of new data sources
ETL Challenges Today

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Discovery & Analytics
Business Intelligence
RDBMSRDBMS
Typical ETL Today
External
ETL Cluster

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RDBMS
Hadoop
RDBMS
Hadoop
ETL Offload with Hadoop
Discovery & Analytics
Business Intelligence
Σ

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 Exponential growth in data,
generated by new consumer devices
 ETL and storage constraints limited
analytics to 1% sample
 Now combined Oracle Exadata and
Cloudera Hadoop delivers analytics
on 100% of data (half a PB per day!)
 Query times reduced dramatically
(i.e. from 4 days to 53 minutes)
 90% reduction of ETL code base
From 1% sampling to 100% analysis
Customer Example: Communications Services
Archive Storage
Data Warehouse
Complex
Correlation
Alerting
Filter
&
Split
Event
Monitoring
Streaming ETL
Streaming ETL
Teleco
m
Services
Before

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 Exponential growth in data,
generated by new consumer devices
 ETL and storage constraints limited
analytics to 1% sample
 Now combined Oracle Exadata and
Cloudera Hadoop delivers analytics
on 100% of data (half a PB per day!)
 Query times reduced dramatically
(i.e. from 4 days to 53 minutes)
 90% reduction of ETL code base
From 1% sampling to 100% analysis
Customer Example: Communications Services
Archive Storage
Data Warehouse
Complex
Correlation
Alerting
Filter
&
Split
Event
Monitoring
Streaming ETL
Streaming ETL
Teleco
m
Services
Before
Data
Warehouse
Alerting
Filter
&
Split
Event
Monitoring
Hadoop
Archive Storage
ETL
Correlation
Stage 1 DWH
Teleco
m
Services
After

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Identifying Potential Opportunities
Do you have a
problem with ETL
performance?
Do you have
potentially valuable
data that you aren’t
using, but might
deliver new insight?
Should you focus
on analyzing
structured data,
unstructured data,
or a combination?
Are Big Data
solutions already
being built as silos?

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Challenges in the Way
& Oracle Strategies
CHALLENGES ORACLE STRATEGY
• Fragmented Solutions • Specialized but integrated data stores and tools
• Difficulty of Self-Service BI • Flexible, guided, automated BI & data discovery
• Data Not Current • Solutions for Just-in-Time well defined data
• Time to ROI / Development Time • Horizontal & industry pre-built solutions, engineered systems
• Growing Diversity of Data & Users • Enterprise solutions for 1000s diverse users, petabytes data
• Manageability, Security, Cost • Centrally managed with advanced security / governance

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Big Data Readiness
The R&D Prototype Stage
 Skills needed
– Distributed data deployment (e.g. Hadoop)
– Python or Java programming with MapReduce
– Statistical analysis (e.g. R)
– Data integration
– Ability to formulate business hypotheses
– Ability to convey business value of Big Data

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Are You Ready for Big Data?
Contact Your Account Team
 Have you delivered mature analytics solutions for
structured data?
 Can Big Data make a difference to the business?
 Have you built a Big Data prototype, built skills, and
proved value?
 Do you have an enterprise integration & deployment
strategy for Big Data?

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Continuous
Innovation
Big Data at Work

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RDBMS
Hadoop
RDBMS
Hadoop
Master diagram
Discovery & Analytics
Business IntelligenceExternal
ETL Cluster
Data
Mart
Data
Mart
Archive
ΣΣEvent
Processing

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Discovery & Analytics
Business Intelligence
RDBMSRDBMS
Master diagram without Hadoop
External
ETL Cluster
Data
Mart
Data
Mart
Archive
ΣEvent
Processing