Watch full webinar here: https://bit.ly/2XXyc3R “Through 2022, 60% of all organisations will implement data virtualization as one key delivery style in their data integration architecture," according to Gartner. What is data virtualization and why is its adoption growing so quickly? Modern data virtualization accelerates that time to insights and data services without copying or moving data. Watch on-demand this webinar to learn: - Why organizations across the world are adopting data virtualization - What is modern data virtualization - How data virtualization works and how it compares to alternative approaches to data integration and management - How modern data virtualization can significantly increase agility while reducing costs

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

2. Introduction to Modern Data Virtualization
Chris Day
Director, APAC Sales Engineering, Denodo
Sushant Kumar
Product Marketing Manager, Denodo

3. Agenda
1. Session Housekeeping
2. Data Management Challenges
3. What is Data Virtualization
4. Customer Story
5. Product Demonstration
6. Q&A
7. Next Steps

4. Challenges of Data Management

5. 5
Current Challenges of Data Management
1. Faster & more complex demands for decision making
 Provide useful information for decision making at all organization levels
 New users with advanced analytical skills and needs: e.g. data scientists
 Solution? Self Service Ini a ves lead by business users, etc. → Either too complex (direct access) or too
costly (specific data marts) , Governance and consistency problems
2. Regulations, enterprise-wide governance & data security
 Tens of new regulations worldwide: tax, finance, privacy, HR, environmental, etc.
 Ensure consistency in semantics of delivered data and data quality
 Enforce security policies
 Solution? Data Governance tools. Separate, sta c system for documenta on → get out of sync easily, don’t
enforce policies and don’t deliver data to users
3. Complexity of DM Infrastructure: IT cost reduction
 Huge data growth, opera on costs → IT is looking for cheaper and more flexible solu ons
 Solution? Cloud, Data Lakes → Increase integra on complexity in the short term. e.g. Gartner says “83% of
Data Lakes projects have failed”

6. 6
IT Architecture is Unmanageable & Brittle because:
Business – IT Dilemma
IT responds by
loosely stitching
together
disparate data
sources
Any changes
break the flow
and affect
business
continuity
Business Wants All of the Data, Now
– So IT creates 100s to 1000s of brittle direct connections and
replicates large volumes of data
Inventory System
(MS SQL Server)
Product Catalog
(Web Service -SOAP)
BI / Reporting
JDBC, ODBC,
ADO .NET
Web / Mobile
WS – REST JSON,
XML, HTML, RSS
Log files
(.txt/.log files)
CRM
(MySQL)
Billing System
(Web Service -
Rest)
ETL
Portals
JSR168 / 286,
Ms Web Parts
SOA,
Middleware,
Enterprise Apps
WS – SOAP
Java API
Customer Voice
(Internet,
Unstruc)

7. 7
- 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.

8. 8
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

9. 9
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
Customer 360
View
Virtual Data
Mart View
J
Application
Layer
Business
Layer
Unified View Unified View
Unified View
Unified View
A
J
J
Derived View Derived View
J
J
S
Transformation
& Cleansing
Data
Source
Layer
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Abstraction

10. Capabilities of Data Virtualization

11. 11
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

12. 12
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

13. 13
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.

14. 14
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

15. 15
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

16. 16
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

17. 17
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

18. Seacoast Bank
Customer Story

19. 19
Customer story - Seacoast Bank
A modern self service BI
Challenges faced
• Maintainingseparatesystemsfor suchfunctionsasbackofficeoperations,datawarehousing,
andloan origination
• Seriesof mergersandacquisitionswereaddingtothecomplexity.
• Adhoc,manualreportingprocess

20. 20
Solution
Business Gains
• DepositandLoanOperations,tomake
timely,accuratedecisions.
• Meettheoperationalandanalytical
needsofmultiplebusinessunits within
theorganization.
• Reducereportingtimefromupto3days
forstaticreportstoas littleas2hours
• Performcriticalbusiness
operations,suchasloan
processing,inreal time.

21. 21
MarkBlanchette,VP and director of Business Technology and Data
Management at Seacoast Bank
Denodo’s datavirtualizationtechnologyhasplayedthemostimportant
roleinenablingourbusinessusers togarnervaluableinformation
throughself-servicereporting.TheDenodoPlatform’scapabilityhas
significantlyincreased thespeedatwhichbusinessiscarriedoutat
SeacoastBank.

22. Product Demonstration
Director, APAC Sales Engineering, Denodo
Chris Day

23. 23
Demo Scenario
Distributed Data:
 Historical sales data offloaded to
Hadoop cluster for cheaper storage
 Marketing campaigns managed in an
external cloud app
 Customer details table, stored in the
DW
1) On-board and expose distributed data
through a single logical layer.
2) Publish a logical view calculating the
impact of a new marketing campaign
by country?
Sources
Combine,
Transform
&
Integrate
Consume
Base View
Source
Abstraction
Sales Campaign Customer
Sales Evolution

24. 1. Data architectures are getting more complex, more
diverse, and more distributed
2. Traditional data integration and management
approaches are too expensive, slow and complex
3. Data virtualization makes it quick and easy to
expose data from multiple source to your users
while still maintaining governance and security…
4. …and enables a wide range of use cases; from self-
service analytics and data services to centralized
data governance and compliance
Key Takeaways

26. Next Steps

27. 27
https://bit.ly/3qEFy8B

28. 28
https://bit.ly/3ixZpTX

29. Thanks!
www.denodo.com info@denodo.com
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