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Agenda
State of Enterprise Information
The Case for Data Virtualization
How Data Virtualization Works
Data Virtualization Adoption Patterns
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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The State Of Enterprise Information
More demanding business users Competition drives faster time-to-information Younger staff want more “do-it-yourself” “IT’s challenges are not my problem.”
Information overload Exponential data volume growth Omnipresent delivery
“Over the top” IT complexity New sources, uses, and enabling technology Layered on byzantine IT infrastructures
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Data Management Trends
Changing role of the Data Warehouse Data warehouse no longer viewed as only focal point for
all data integration
Lower latencies required Information needs moving toward real time
Rising “fit-for-purpose” storage and processing Appliances, MPP, NoSQL
Data Quality being addressed at every layer Source, Consolidation, Virtual, and Visualization
Clouds are approaching… Most enterprises looking to leverage cloud computing
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Agenda
State of Enterprise Information
The Case for Data Virtualization
How Data Virtualization Works
Data Virtualization Adoption Patterns
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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The Challenge
Big DataFiles Packaged Applications
Web Services
RDBMS
BI, CPM, andReporting
Custom and Composite Apps
Portals andDashboards
SOAInitiatives
SourceData
Siloed & Rigid
ConstantChange
BusinessSolutions
Data IntegrationChallenge
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Traditional Physical Data Consolidation
Big DataFiles RDBMS Web Services
Packaged Applications
Enterprise DataWarehouse
PhysicalData Marts
Physical OperationalData Stores
PhysicalIntermediate Stores & ETL Middleware
SourceData
BusinessSolutions
BI, CPM, andReporting
Custom and Composite Apps
Portals andDashboards
SOAInitiatives
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Traditional Physical Data Consolidation
Big DataFiles RDBMS Web Services
Packaged Applications
BI, CPM, andReporting
Custom and Composite Apps
Portals andDashboards
SOAInitiatives
Enterprise DataWarehouse
PhysicalData Marts
Physical OperationalData Stores
PhysicalIntermediate Stores & ETL Middleware
SourceData
BusinessSolutions
More silos & complexity Slows future IT progress
Physical consolidation Forces the business to wait
longer for solutions
Wait, wait, wait!
Uncontrolled data replication Reduced data quality Significant hidden costs
$$$
$ $$$
$$$
$$
$$$
$
Batch integration Delay real-time information
Customer X
Invoice
UNPAID
Customer X
Invoice
PAID IN FULL
Batch Data
On-Demand Data
OLD
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
Data Integration Architectures and Patterns: Build a Portfolio to Address the Range of Needs
Physical Movement and Consolidation
(e.g., ETL)
Abstraction/Virtual Consolidation
(Data Federation)
Change-Capture and Propagation (Replication or
Messaging)
Common Metadata (Location, Format, Structure, Quality, Meaning)
Common Connectivity (Full range of source/target types)
BI Tools/Apps. Master Data Mgmt. Operational Apps. Interenterprise
Leading organizations support multiple styles of data integration and delivery to address a range of business requirements — breadth enables leverage and agility.
Com
mon D
esign, Adm
in., Go
vernance
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Physical Movement and Consolidation
(ETL, CDC)
Abstraction / Virtual Consolidation
(Data Federation)
Middle-ware ETL CDC Data Virtualization EAI / ESB
Purpose
Attribute
How Data Virtualization Differs
Synchronization and Propagation
(Messaging)
DB DB
ScheduledEventDriven
Application ApplicationDB Application
On DemandEventDriven
DB DB
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Traditional Physical Data Consolidation
Big DataFiles RDBMS Web Services
Packaged Applications
BI, CPM, andReporting
Custom and Composite Apps
Portals andDashboards
SOAInitiatives
PhysicalData Marts
Physical OperationalData Stores
Enterprise DataWarehouse
PhysicalIntermediate Stores & ETL Middleware
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Data Virtualization Increases Agility
Big DataFiles RDBMS Web Services
Packaged Applications
BI, CPM, andReporting
Custom and Composite Apps
Portals andDashboards
EnterpriseSearch
PhysicalData Marts
Physical OperationalData Stores
VirtualData Marts
Virtual OperationalData Stores
Enterprise DataWarehouse
DataVirtualization
PhysicalIntermediate Stores & ETL Middleware
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Shared Data Services & Relational Views Further Extend Flexibility and Agility
Big DataFiles RDBMS Web Services
Packaged Applications
PhysicalData Marts
Physical OperationalData Stores
Virtual Data Layer
VirtualData Marts
Virtual OperationalData Stores
Web Data Services& Relational Views
Enterprise DataWarehouse
CompositeInformation
Server
PhysicalIntermediate Stores & ETL Middleware
BI, CPM, andReporting
Custom and Composite Apps
Portals andDashboards
SOAInitiatives
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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A Complete Data Integration Architecture
Big DataFiles RDBMS Web Services
Packaged Applications
BI, CPM, andReporting
Custom and Composite Apps
Portals andDashboards
SOAInitiatives
Physical Data Consolidation Layer
Virtual Data Layer
VirtualData Marts
Virtual OperationalData Stores
Shareable Data Services& Relational Views
PhysicalData Marts
Physical OperationalData Stores
Enterprise DataWarehouse
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Forrester Data Management Reference Architecture
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Agenda
State of Enterprise Information
The Case for Data Virtualization
How Data Virtualization Works
Data Virtualization Adoption Patterns
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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How Data Virtualization Works – Example Scenario
1) I need to build an application that looks like this…
2) The view or data service needs to look like this…
3) And the data comes from these sources, in these formats…
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Composite Information Server
Studio
Data Discovery and Design
Design Steps1. Discover data and relationships
2. Model individual view/service
3. Validate view/service
4. Modify as required
BenefitsFaster time to solution
Easy to learn and use
Extensible / reusable objects
Discovery
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Composite Information Server
Data Virtualization Production
Production Steps1. Application invokes
request
2. Optimized query (single statement) executes
3. Deliver data in proper form
BenefitsUp-to-the-minute data
High performance
Less replication required
Optimizer
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Composite Information Server
Data Virtualization Production with Caching
Production Steps1. Cache essential data
2. Application invokes request
3. Optimized query (leveraging cached data) executes
4. Deliver data in proper form
BenefitsRemoves network constraints
7-24 availability
Optimal performance
CacheOptimizer
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Agenda
State of Enterprise Information
The Case for Data Virtualization
How Data Virtualization Works
Data Virtualization Adoption Patterns
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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DataFederation DW Extension
Cloud DataIntegration
Data Virtualization Adoption Patterns
Data VirtualizationLayer
Big DataIntegration
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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DataFederation
Data Federation for Business Intelligence
“My application requires data from multiple incompatible sources.”
Project Manager
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“My data warehouse does not contain all the data required for the reports we need to build.”
Data WarehouseExtension
Data Warehouse Extension for 360o View
Data WarehouseOwner
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Data VirtualizationLayer
Data Virtualization Layer for Business & IT Agility
“How do I build an agile data layer for easy data access and delivery.”
IT Director
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Cloud DataIntegration
Cloud Data Integration for IT Extensibility
“I need to integrate data between on-premise systems and applications running in the cloud.”
CIO
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“More and more of my data now lives in MPP and Hadoop sources. How do I combine big data with traditional data for analysis?
Big DataIntegration
Big Data for Analytics
Business Analyst
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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DataFederation DW Extension
Cloud DataIntegration
Data Virtualization Adoption Patterns
Data VirtualizationLayer
Big DataIntegration
Semantic Abstraction Federated Query Loose Coupling Caching Location Independence
= DataVirtualization
© 2011 Composite Software, Inc. / Composite Proprietary and Confidential
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Composite Software Contact
For more information please contact:
Pamela Sotnick Director, Federal AccountsMobile [email protected]
Katy MannDirector, Federal AccountsMobile 301.452.7042 [email protected]
David [email protected]
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