1. By Bhaven Chavan
bhaven2001@yahoo.com
6/23/2016
Data Virtualization
6/23/2016
Confidential | 2016
DISCLAIMER
Note: It is understood that the material in this presentation is intended for general information only and should
not be used in relation to any specific application without independent examination and verification of its
applicability and suitability by professionally qualified personnel. Those making use thereof or relying thereon
assume all risk and liability arising from such use or reliance.
2. Agenda
• High level walkthrough of the Data Virtualization concepts and its possible
utilization:
• What is Data Virtualization?
• Why use Data Virtualization?
• When not to use Data Virtualization?
• What functionality it provides?
• Data Virtualization Overview
• Data Virtualization and Big Data/NoSQL Overview
• *Drawbacks*
• Q&A
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3. What is Data Virtualization?
• Data Virtualization is an umbrella term used to describe any
approach to data management that allows an application to retrieve
and manipulate data without requiring technical details about the
data, such as how it is formatted or where it is physically located.
• Data virtualization is a technique to deliver the data by consuming
many desperate data sources (internal/external) with a simplified,
integrated view of trusted data within enterprise using real-time or
near real time mechanism to achieve the business goals that support
business transactions, analytics, predictive analytics, and other
workloads and pattern.
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4. Why use Data Virtualization?
• Today’s complex world with so much data, business is looking for
instant access to all the complex data irrespective of the location to
meet the immediate market needs with an Agile manner.
• It helps in reducing the cost in data replication and data
consolidation.
• It adds value in Data Governance.
• Improves the Data Quality.
• It reduce data storage required.
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5. When not to use Data Virtualization?
• Data Virtualization is not the solution to every data integration
problems. Such as, persisting need of the data in a warehouse
(UDL/ODS) or data-mart, along with E-T-L or E-L-T is better solution for
specific use case. Sometimes a hybrid solution is the right answer.
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6. What functionality it provides?
• Virtualized Data Access
• It connects to the different data sources and make them accessible from a common data
access point.
• Data Transformation
• It transforms improved data quality and it reformats the source data the way consumer
needs.
• Data Federation
• It combines results set from across the multiple heterogeneous source systems.
• Data Delivery
• It publishes result sets as views and/or data services executed by client application or users
when requested.
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7. Data Virtualization Overview
Data Virtualization Server
OLTP
Databases
Data
Warehouse &
Data Marts
Applications
ASSET JMS SQL
Unstructured
Data
XSLT
ESB
SOAP EXCEL
Big Data
Store
Social
Media
Data
HIVE JSON
Private
Data
External
Data
Prop.
OLTP
Application
Analytics &
Reporting
ODBC/
JDBC/SQL JDBC/SQL
Service API
XML/SOAP REST/JSON
Mobile App Website
XQuery DAX/MDX
Dashboard
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10. Data Virtualization and Big Data/NoSQL
• It unleashes the full value of Big Data for
analytics
• It speeds up development on Big Data
sources
• It offers an evolutionary adoption of Big
Data
• It makes Big Data available to everyone
• Higher Big Data ROI
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11. NoSQL as Sand Box
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OLTP
Databases Reporting &
Analytics
SQL SQL SQL
SQL
NoSQL
Data Staging
Area
Data
Warehouse
Data Marts
Data
Virtualization
Server
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Confidential | 2016
12. NoSQL for Storing Cold Data
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OLTP
Databases
SQL SQL SQL
SQL
NoSQL
Data Staging
Area
Data
Warehouse
Data Marts
Reporting &
Analytics
Data
Virtualization
Server
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Confidential | 2016
13. NoSQL as Staging Area
6/23/2016
Data
Virtualization
Server
OLTP
Databases
SQL SQL
SQL
NoSQL
Data Staging
Area
Data
Warehouse
Data Marts
Reporting &
Analytics
6/23/2016
Confidential | 2016
14. NoSQL as Extra Data Warehouse Database
6/23/2016
OLTP
Databases
SQL SQL
SQL
SQL
NoSQL
Data Staging
Area
Data
Warehouse
Data Marts
Reporting &
Analytics
Data
Virtualization
Server
6/23/2016
Confidential | 2016
16. Drawbacks
• Another/A new DataStore in production to take care of.
• May impact Operational systems response time, particularly if under-
scaled to cope with unanticipated user queries or not tuned early on.
• Does not impose heterogeneous data model, meaning the user has to
interpret the data, unless combined with Data Federation and business
understanding of the data.
• Requires a defined Governance approach to avoid budgeting issues with
the shared services.
• Not suitable for recording the historic snapshots of data. Data
Warehouse is better for this.
• Change management “ is a huge overhead, as any changes need to be
accepted by all applications and users sharing the same virtualization kit.
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