Data virtualization (DV) is a modern data integration strategy that simplifies access to distributed data without replication, supporting various analytics and governance needs. Its usage is increasingly necessary as data becomes more distributed, allowing organizations to quickly prototype and deploy analytics datasets while enhancing self-service capabilities for end users. DV serves as a vital component of hybrid data architectures, providing a holistic view and necessary infrastructure for effective data management and integration.

1. Advancing Cloud, Analytics, and
Data Science with Logical Data Fabric
The Agile Data Management and Analytics Conference

2. #DenodoDataFest
Data Virtualization as a Data Management
Strategy for Advanced Analytics
Sr. Research Director, TDWI
P hi l ip Rus s om , P hD.

3. Today’s
Discussion
Take-Aways
Data virtualization (DV) is a modern approach to data integration
DV usage is rising as the distribution of data gets more extreme
DV provides a development environment that models distributed
data as simpler, unified structures that are easier to use
DV also provides a deployment platform that serves as a data
access point for many users and apps
DV addresses new requirements for analytics, real-time, self-
service, architecture, governance, development methods, etc.
Summary
Please Tweet - @prussom #TDWI @Denodo #DataVirtualization

4. DEFINING
Data Virtualization (DV)
DV provides abstraction and service layers
Integrates heterogeneous and distributed data without replicating it
Creates “virtual” or “logical” layers that unify data to support many
users and apps
DV is a platform type for modern data integration
The usual transformation and quality functions: ETL, federation,
messaging…
But without the latency, redundancy, and rigidity of older approaches
A good DV platform supports query planning, in-memory functions,
business glossary, self-service, and strategies for optimizing cross-
platform performance

5. Most organizations surveyed recently
by TDWI are using Data Virtualization
With DM for AA in your organization, what data and analytics
tools, techniques, and platforms are supported today?
37% of respondents use Data Virtualization for Advanced Analytics
What data management capabilities do you need for successful
advanced analytics?
33% of respondents consider DV to be a success factor for AA
POINT – Data virtualization is real
Organizations use it, because it works and it has value
You should depend on DV, for analytics & other use cases
SOURCE: 2020 TDWI Best Practices Report on Data Management for Advanced Analytics

6. Depend on Data Virtualization
TO PROVIDE USEFUL FUNCTIONS IN MANY CONTEXTS
Logical/virtual layer is insulation for evolving data
architectures, especially as they go cloud/hybrid
Virtual views can be biz-friendly for self-service
or big picture of whole data environment
DV platform can be a central, shared “hub” for:
Views, metadata, catalog, interfaces, gov, security…
DV is great for rapid prototyping of datasets
DV services = batch to real time execution

7. DV as Data Integration
Infrastructure
Data virtualization is a form of data integration
Along with ETL/ELT, replication, synchronization,
quality, metadata, cataloging, event processing
DI infrastructure is not modern or complete
without full DV functionality
DV enhances DI infrastructure immensely
Virtual views, services, multi-latency, diverse data
semantics, data catalog

8. DV as enabler for Real-Time Analytics
Operational reporting
Performance management
Manager dashboards
Embedding analytics
in operational applications
Business activity monitoring and surveillance
DV can complement or replace middleware,
messaging, event processing

9. DV as enabler of Self-Service Practices
Self-service data is a leading requirement for
business end-users
They are influential, so don’t ignore their demands
Support all the self-service practices the biz wants
SS data access, data prep, data viz, analytics
Self-service FAILS without biz-friendly semantics
Business metadata
Data catalog
DV models data via virtual views in ways that
self-service users need

10. DV enables Modern Analytics Methods
AGILE – DV for rapid prototyping of analytics datasets
Virtual modeling in DV tool, with minimal data movement
Get a prototype in front of your business sponsor sooner
Iterative improvement of datasets is equally fast
LEAN – DV frees you from excess development steps
DV views are self documenting
Less testing and deployment steps before production
Most maintenance of datasets done from DV console

11. DV is a must for Hybrid Data Arch (HDA)
A broad DV view of distributed data reveals architecture
How else can you get the “big picture” of multiplatform and
hybrid data architectures, as in a modern data warehouse?
One DV view has both academic and practical value
Documentation for architects, data governors, IT
Also, a practical point of data access for users & apps
DV (along with other DI & semantics) can stitch together…
Today’s complex data environments
Hybrid data architectures

12. Summary
Data virtualization (DV) is a modern approach to data integration
DV usage is rising as the distribution of data gets more extreme
DV provides a development environment that models distributed
data as simpler, unified structures that are easier to use
DV also provides a deployment platform that serves as a data
access point for many users and apps
DV addresses new requirements for analytics, real-time, self-
service, architecture, governance, development methods, etc.

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