The document discusses a webinar series on data virtualization that addresses challenges in data integration, highlighting its benefits in providing real-time access to diverse data sources without physical movement. Key themes include the evolution of data architectures, the importance of a data abstraction layer, and practical use cases demonstrating the flexibility and agility that data virtualization offers. It emphasizes that data virtualization can significantly reduce delivery times for analytics while hiding the complexity of underlying data infrastructure.

1. DATA VIRTUALIZATION PACKED LUNCH
WEBINAR SERIES
Sessions Covering Key Data Integration Challenges
Solved with Data Virtualization

2. Data Virtualization: An Introduction
Paul Shimabukuro
Sales Engineer, Denodo
Paul Moxon
SVP Data Architectures & Chief Evangelist, Denodo

3. Agenda
1. Data Virtualization: An Introduction
2. Data Virtualization – How it works
3. Product Demo
4. Q&A
5. Next Steps

4. Data Virtualization: An Introduction
4

5. Data Integration – A Classic Approach
5
Operational
Data Stores
Staging Area Data Warehouse Data Marts Analytics and
Reporting
ETLETLETL

6. Data Integration – A Modern Data Ecosystem
6

7. The Data Integration Challenge
7
Manually access different
systems
IT responds with point-to-
point data integration
Takes too long to get
answers to business users
MarketingSales ExecutiveSupport
Database
Apps
Warehouse Cloud
Big Data
Documents AppsNo SQL
Businesses are reporting that integrating data from
silos to support real-time insights has become a
nightmare, especially when supporting large and
complex data sets
Big Data Fabric 2.0 Drives Data Democratization, May 9, 2019

8. 8
The Data Integration Challenge
It is difficult to integrate numerous
on-premises and cloud data sources.
Traditional tools cannot integrate streaming
data and data-at-rest in real time.
It is difficult to maintain consistent data access
and governance policies across data siloes.
Traditional data integration is extremely
resource intensive.

9. The Solution – A Data Abstraction Layer
9
Abstracts access to
disparate data sources
Acts as a single repository
(virtual)
Makes data available in
real-time to consumers
DATA ABSTRACTION LAYER
“Enterprise architects are finding that traditional
data architectures are failing to meet new business
requirements, especially around data integration for
streaming analytics and real-time analytics.”
The Forrester Wave: Enterprise Data Virtualization, Jan 12, 2018
DATA VIRTUALIZATION PLATFORM

10. 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.

11. Data Virtualization
11
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 StructuredMore 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
“Data virtualization
integrates disparate
data sources in real
time or near-real
time to meet
demands for
analytics and
transactional data.”
– Create a Road Map For A
Real-time, Agile, Self-
Service Data Platform,
Forrester Research, Dec 16,
2015

12. 12
How Does It Work?
Development
Lifecycle Mgmt
Monitoring &
Audit
Governance
Security
Development
Tools and SDK
Scheduled Tasks
Data Caching
Query Optimizer
JDBC/ODBC/ADO.Net SOAP / REST WS
U
Business
View
Data Mart
View
J
Application
Layer
Business
Layer
Unified
View
Unified
View
Unified
View
Unified
View
A
J
J
Derived
View
Derived
View
J
JS
Transformation
& Cleansing
Data
Source
Layer
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Abstraction

13. Data Virtualization Connects the Users to the Data That They Need
1. Data Virtualization allows you to connect to (almost) any data source
2. You can combine and transform that data into the format needed by the
consumer
3. The data can be exposed to the consumers in a format and interface
that is usable by them
• Typically consumers use the tools that they already use – they don’t have to learn new
tools and skills to access the data
4. All of this can be done without copying or moving the data
• The data stays in the original sources (databases, applications, files, etc.) and is
retrieved, in real-time, on demand
13
Cliffs Notes version (TL;DR)

14. Gartner – The Evolution of Analytical Environments
14
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
2010s2000s
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
ID: 342254
Fragmented/
nonexistent analysis
› Multiple sources
› Multiple structured sources
Fragmented analysis
› "Collect the data" (Into
› different repositories)
› New data types,
› processing, requirements
› Uncoordinated views

15. 15
Gartner – Logical Data Warehouse
“Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry
Cook, Gartner April 2018
DATA VIRTUALIZATION

16. − Market Guide for Data Virtualization, Gartner, November, 16, 2018
“Through 2022, 60% of all organizations will
implement data virtualization as one key delivery
style in their data integration architecture.”
16

17. 17
Denodo ‘Use Case’ Categories
Customer Centricity
APIs/Services
✓ Data as a Service
✓ Microservices/containers
✓ API Data Services
✓ Application Migration
Machine Learning
✓ Data Catalog
✓ Metadata management
✓ Universal data access
✓ Governance, Risk, Compliance (GRC)
✓ Data Masking/Data Privacy
✓ Auditing/data lineage
✓ Hybrid/Multicloud Integration
✓ Cloud Data Analytics
✓ Cloud IT Modernization

18. Product Demonstration
Data Virtualization – An Introduction
18
Sales Engineer, Denodo
Paul Shimabukuro

19. 19
Demo Architecture
What’s the impact of a new
marketing campaign for each
country?
▪ Historical sales data offloaded
to Hadoop cluster for cheaper
storage
▪ Marketing campaigns managed
in an external cloud app
▪ Country is part of the customer
details table, stored in the DW
Sources
Combine,
Transform
&
Integrate
Consume
Base View
Source
Abstraction
join
group by country
join
Sales Campaign Customer

20. Demo
20

21. Key Takeaways
21

22. Key Takeaways
22
FIRST
Takeaway
Data Virtualization is a key technology when building a
modern data architecture
SECOND
Takeaway
It provides flexibility and agility and reduces the time to
deliver data to the business by up to 10X
THIRD
Takeaway
Data Virtualization hides the complexity of a constantly
changing data infrastructure from the users
FOURTH
Takeaway
In doing so, it allows you to introduce new technologies,
formats, protocols, etc. without causing user disruption

23. Q&A

24. 24
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
GET STARTED TODAY

25. Thank you!
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