Watch full webinar here: https://bit.ly/3puUCIc What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers. Watch on-demand this session to learn: - What is Data Virtualization? - Why do I need Data Virtualization in my organization? - How do I implement Data Virtualization in my enterprise? Where does it fit?

1. DATA VIRTUALIZATION
Packed Lunch Webinar Series
Sessions Covering Key Data Integration
Challenges Solved with Data Virtualization

2. Data Virtualization: An Introduction
Robin Tandon
Product Marketing
Director | Denodo
Paul Shimabukuro
Sales Engineer
Denodo

3. Agenda
1. Data challenges in the 21st century
2. What is Data Virtualization?
3. Benefits of Data Virtualization
4. How Data Virtualization Works
5. Key takeaways
6. Q&A

4. Challenges of Data Management

5. 5
2020’s Data Facts
Rising Volume of data
▪ 90% of the data have been produced in the past 2 years
▪ 40 zettabytes of Data by end 2020 (5 200GB / person on earth)
▪ Every person will be generating 1.7 MB data / second In 2020
▪ It will take 181 million years for a person to download all those Data
Rising Business challenges with Data
▪ Poor data quality costs business between $9 M to $14 M a year
▪ Bad data is estimated to cost US only $3 trillion a year
▪ 97% of organization are investing in AI & Big Data
▪ 93% have multi-cloud & hybrid strategy
▪ Data Scientists waste 75% looking for Data
Sources: 2020, Capgemini, IBM, EDC …

6. 6
• Social Media
• Mobile Devices
• Increased Internet
commerce/transac
tions
• Networked
devices/sensors
New sources of data New repositories
•Images
•Streamed
data
•Video/audio
•Parkay
New types
• Citizen analysts
• Customer
demands
• AI/ML
• Predictive
Analytics
• Data Science
Increase demand
• PPI
• Reporting needs
(AML/KYC, HEDIS,
etc.)
• GDPR
• HR, Privacy, Tax
Growing Regulatory concerns
• SaaS/PaaS
• Cloud based data
• Governance
challenges
• More apps
Increasingly complex
More Data More Insight
21st Century Data Challenges
•Data Lake
•Snowflake
•Queues
TIME & Cost

7. 7
Information provision flow
Discovery/Requirements
Analysis
Prototype
Feedback
Develop
Test
Feedback
Release
Same old story

8. 8
How do I handle data
Gartner – The Evolution of Analytical Environments
This is a Second Major Cycle of Analytical Consolidation
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Operational
Application
Operational
Application
Cube
Operational
Application
Cube
? Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
1980s
Pre EDW
1990s
EDW
2010s
2000s
Post EDW
Time
LDW
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Lake
?
LDW
Data Warehouse
Data Lake
Marts
ODS
Staging/Ingest
Unified analysis
› Consolidated data
› "Collect the data"
› Single server, multiple nodes
› More analysis than any
one server can provide
©2018 Gartner, Inc.
Unified analysis
› Logically consolidated view of all data
› "Connect and collect"
› Multiple servers, of multiple nodes
› More analysis than any one system can provide
Fragmented/
nonexistent analysis
› Multiple sources
› Multiple structured sources
Fragmented analysis
› "Collect the data" (Into
› different repositories)
› New data types,
› processing, requirements
› Uncoordinated views

9. 9
Can you consolidate all your data to a single location ?
Data warehouse Data lake Cloud

10. 10
Data anti-gravity
▪ Geography
▪ Ownership
▪ Technology

11. 11
Data is distributed

12. 12
Typical Data Architecture

13. What is Data Virtualization..?

14. 14
Source: “Gartner Market Guide for Data Virtualization, November 16, 2018”
Data virtualization can be used to create virtualized
and integrated views of data in-memory rather
than executing data movement and physically storing
integrated views in a target data structure. It
provides a layer of abstraction above the physical
implementation of data, to simplify query logic.

15. 15
What is Data Virtualization?
Consume
in business applications
Combine
related data into views
Connect
to disparate data sources
2
3
1
DATA CONSUMERS
DISPARATE DATA SOURCES
Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users
Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word...
Analytical Operational
Less Structured
More Structured
CONNECT COMBINE PUBLISH
Multiple Protocols,
Formats
Query, Search,
Browse
Request/Reply,
Event Driven
Secure
Delivery
SQL,
MDX
Web
Services
Big Data
APIs
Web Automation
and Indexing
CONNECT COMBINE CONSUME
Share, Deliver,
Publish, Govern,
Collaborate
Discover, Transform,
Prepare, Improve
Quality, Integrate
Normalized views of
disparate data

16. 16
Modern Data Virtualization
Data Virtualization enhanced with data management, automation and AI
➢ Delivers data more quickly than direct queries
➢ Leverages AI to accelerate performance and enhance the user experience
➢ An active data catalog to explore and govern data in real time
➢ Empowers data scientists with an integrated data science notebooks
➢ Flexible support for hybrid and multi-cloud architectures
➢ Employs automation to speed cloud deployment and management
➢ Leverages SSO and fine grain permissions to secure data assets

17. 17
Six Essential Capabilities of Data Virtualization
4. Self-service data services
5. Centralized metadata, security &
governance
6. Location-agnostic architecture for
multi-cloud, hybrid acceleration
1. Data abstraction
2. Zero replication, zero relocation
3. Real-time information

18. 18
1. Data abstraction
Abstracts access to disparate data sources.
Acts as a single virtual repository.
Abstracts data complexities like location,
format, protocols
…hides data complexity for ease of data access by business
Enterprise architects must revise their data architecture to meet
the demand for fast data.”
– Create a Road Map For A Real-time, Agile, Self-Service Data
Platform, Forrester Research

19. 19
2. Zero replication, zero relocation
…reduces development time and overall TCO
The Denodo Platform enables us to build and deliver data
services, to our internal and external consumers, within a
day instead of the 1 – 2 weeks it would take with ETL.”
– Manager, Enervus
Leaves the data at its source; extracts only what is
needed, on demand.
Diminishes the need for effort-intensive ETL
processes.
Eliminates unnecessary data redundancy.

20. 20
3. Real-time information
Provisions data in real-time to consumers
Creates real-time logical views of data across many
data sources.
Supports transformations and quality functions
without the latency, redundancy, and rigidity of legacy
approaches
…enables timely decision-making
Denodo’s data fabric design relies on data virtualization
to provide integrated data quickly to business users to
effect faster outcomes..”
– Gartner Magic Quadrant for Data Integration Tools, 18 August’ 2020

21. 21
4. Self-service data services
Facilitates access to all data, both internal and external
Enables creation of universal semantic models reflecting
business taxonomy
Connects data silos to provide best available information to
drive business decisions
…enables information discovery and self-service
Impressively quick turn around time to "unlock“ data from
additional siloes and from legacy systems - Few vendors (if any) can
compete with Denodo's support of the Restful/Odata standard -
both to provide data (northbound) and to access data from the
sources (southbound).”
– Business Analyst, Swiss Re

22. 22
5. Centralized metadata, security & governance
Abstracts data source security models and enables single-point
security and governance.
Extends single-point control across cloud and on-premises
architectures
Provides multiple forms of metadata (technical, business,
operational) to facilitate understanding of data.
…simplifies data security, privacy, audit
Our Denodo rollout was one of the easiest and most successful rollouts of critical
enterprise software I have seen. It was successful in handling our initial, security,
use case immediately, and has since shown a strong ability to cover additional
use cases, in particular acting as a Data Abstraction Layer via it's web service
functionality.”
– Enterprise Architect, Asurion

23. 23
6. Location-agnostic architecture for multi-cloud, hybrid acceleration
Optimizes costs by migrating data, applications, and analytics
workloads to cloud without impacting the business
Enables creation of hub architecture to support integration of
data across mixed workloads.
End-to-end management of migrations/promotions and
continuous delivery processes.
…enables cloud adoption
Impressively quick turn around time to "unlock“ data from
additional siloes and from legacy systems - Few vendors (if any) can
compete with Denodo's support of the Restful/Odata standard -
both to provide data (northbound) and to access data from the
sources (southbound).”
– Business Analyst, Swiss Re

24. 24
Reference Architecture
IT: Flexible Source Architecture
Business: Flexible
Tool Choice

25. 25
Reference Architecture
IT: Flexible Source Architecture
Business: Flexible
Tool Choice
Business can
now make
faster & more
sophisticated
decisions as
all data
accessible by
any tool of
choice
IT can now
move at a
cadence
that suits
speed w/o
affecting
business

26. How Data Virtualization Works?

27. 27
Denodo Platform Architecture
DATA CATALOG
Discover - Explore - Document
DATA AS A SERVICE
RESTful / OData
GraphQL / GeoJSON
BI Tools Data Science Tools
SQL
CONSUMERS
DATA VIRTUALIZATION
CONNECT
to disparate data
in any location, format
or latency
COMBINE
related data into views
with universal semantic
model
CONSUME
using BI & data science
tools, data catalog,
and APIs
Self-Service
Hybrid/
Multi-Cloud
Data
Governance
Query
Optimization
AI//ML
Recommendations
Security
LOGICAL
DATA
FABRIC
SOURCES
Traditional
DB & DW
150+
data
adapters
Cloud
Stores
Hadoop
& NoSQL OLAP Files Apps Streaming SaaS
1
2
3
4
5
6
7
8
9 10
11

28. 28
Denodo Platform – How does virtualization work..?
DATA CATALOG
Discover - Explore - Document
DATA AS A SERVICE
RESTful / OData
GraphQL / GeoJSON
BI Tools Data Science Tools
SQL
CONSUMERS
LOGICAL
DATA
FABRIC
SOURCES
Traditional
DB & DW
150+
data
adapters
Cloud
Stores
Hadoop
& NoSQL OLAP Files Apps Streaming SaaS
U
Customer 360
View
Virtual Data
Mart View
J
Unified
View
Unified
View
Unified
View
Unified
View
A
J
J
Derived
View
Derived
View
J
J
S
Transformation
& Cleansing
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Abstraction
CONNECT
COMBINE
CONSUME

29. Benefits – Why should I care

30. 30
Benefits of Using Data Virtualization
Easier & faster access to trusted data
• For Business Users
• Simplicity: Users don’t need to navigate the complexity of the architecture. Where is
data (on-prem, cloud, multi-cloud)? How to Access it? Which location has priority?
• Agility: All data is securely delivered from a single (virtual) system
• Accessibility: Data is accessible in a variety of formats (SQL, REST, OData, GraphQL)
and in a web-based Data Catalog, regardless of original format and location
• Common Semantic Layer: All users see the same definitions and data, providing data
consistency
• Governed Self-Service: Users can use their own tools (BYOT) to access and query the
data that is governed, secure, and trusted data.

31. 31
Benefits of Using Data Virtualization
Faster, cheaper, simpler, easier to secure and govern
• For IT
• Abstraction: Decouples storage and processing engines from the delivery of data
• Flexibility: Allows IT to change technologies and move data without service
interruptions
• Security: Centralized governance and security controls for all data assets
• Governance: The data accessed by the users can be governed, secured, and managed
so that users are accessing known, trusted, and approved data sets.
• Accelerated Delivery: As data is not be replicated to a staging area or data mart for
use, it is significantly quicker (up to 90% quicker) to deliver the data needed by the
users.

32. 32
Using The Denodo Platform; Published – 1st October 2021
Forrester Total Economic ImpactTM of Data Virtualization
Forrester Total Economic ImpactTM of Data Virtualization Using the Denodo Platform, October 1, 2021

33. 33
Total Economic Impact of Data Virtualization
“Denodo is the right tool for
collaborative, flexible work
and giving the business more
room to create flexible
applications for the future.
It’s proved so until today.”
— IT Manager, Manufacturing
ROI
408%
BENEFITS
$6.8M
PAYBACK
<6 months
Forrester has determined the
following three-year impact:

34. 34
Data Virtualization use cases
From Data Storage & Management, to Data Consumers, going through Data Governance & Security
Decision
(Real time)
Single View
(Customer 360)
Agile BI
(Self-service)
Data Science
(ML & AI)
APPS
(Mobile & web)
Mergers &
Acquisitions
Data
Marketplace
Compliances
(IFRS17, GRC)
Data
Security
APIfication
(& SQLification)
Unified Data
Layer
Agility
& Simplicity
Real-time
Delivery
Data
Abstraction
Zero
Replication
Data
Governance
Sophisticated
Optimizations
Logical Data
Warehouse/Lake
Big Data
Fabric
Hybrid
Data Fabric
Data
Integration
Data
Migration
Refactoring &
Replatforming
Data Consumption
Data Storage & Management
Data Governance, Manipulation & Access
Sales
HR
Executive
Marketing
Apps/API
Data
Science
AI/ML

35. Product Demonstration
35
Paul Shimabukuro
Sales Engineer

36. Demo Scenario
Use Case
• Integrate data from 3 disparate sources
to determine the impact of a marketing
campaign on total sales in each country
in which they do business.
Data Sources
▪ Historical sales data stored in data lake
▪ Marketing campaigns managed in an
external cloud app
▪ Customer data stored in the EDW
Sources
Combine,
Transform
&
Integrate
Consume
Base View
Source
Abstraction
Sales Campaign Customer
Sales Evolution

37. 37
Demo

38. Key Takeaways

39. Reduce Complexity, Time and Money
Data architectures
are getting more
complex, more
diverse, and more
distributed
Traditional data
integration and
management
approaches are too
expensive, slow and
complex
Multiple Use Case Support
Enables a wide range
of use cases; from
self-service analytics
and data services to
centralized data
governance and
compliance,
innovation platform.
Abstraction helps
Presenting unified
business consumable
informational assets.
Separation of storage
and access.
Governance
Governed access
across silos of data.
Agility
Faster provisioning
of business
consumable data
sets. Rapid
prototyping
capabilities and
empowerment of
business users.

40. Q&A

41. Forrester Study: The Total
Economic Impact™ of
Data Virtualization
Using The Denodo Platform
https://www.denodo.com/ForresterTEI
Download TEI Study:

42. 42
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43. Thanks!
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