Modernizing Integration
with Data Virtualization
Fusion Alliance & Denodo
WEBINAR

Speakers
2
Keath Lewin
Technology Advocate Customer Success
Denodo
Saj Patel
Vice President, Data Practice
Fusion Alliance
Mike Mappes
Senior Strategic Data Management & Analytics Consultant
Fusion Alliance

1. Introduction to Fusion Alliance
2. Data Virtualization Platform and Overview
3. Building the case for Data Virtualization
4. The Fusion Data Virtualization Discovery Workshop
5. Questions
6. Additional Resources
Agenda
3

4
About Fusion &
The Data Practice

Fusion is your digital
transformation partner
We leverage data insights, experience design,
and technology solutions to reimagine how you
connect with your customers.
5

Who is Fusion Alliance
6
INDIANAPOLIS, IN
CINCINNATI, OH
COLUMBUS, OH
3 OFFICES
WE’LL MEET
YOU WHERE
YOU ARE
HEALTHCARE INSURANCE FINANCIAL
RETAIL GOVERNMENT EDUCATION
ENERGY
SERVING NATIONAL AND GLOBAL
BUSINESSES ACROSS MULTIPLE INDUSTRIES

Overview of Fusion Services
7
Technology
• Technology Strategy
• Application Development
• API Consulting
• Emerging Technologies
• Software Testing
Cloud
• Cloud Strategy
• Cloud Development
• Cloud Infrastructure
• Identity & Access Management
• Dynamics & Infrastructure
Data
• Strategic Data Management
• Data Integration &
Architecture
• BI & Analytics
• AI & Machine Learning
Digital
• Customer Experience
Consulting
• Marketing Operations
• Web Platform Development
• Mobile App Development

8
Where Fusion Helps with
Data Management

The Future of Data Management
Trending topics are causing a rethinking of what is deemed essential for data management.
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360°
CUSTOMER
360
Requires organizations to embrace ‘Data as an Asset’ and assess data capabilities broadly.

How we support your data evolution
10
Establish a big-picture data
strategy and a roadmap to get
there. Jumpstart your
organizational capabilities
with data governance,
stewardship, quality, and
metadata management.
Strategize
Evaluate and implement a
modern data platform.
Establish your enterprise data
architecture. Rationalize the
right data management
technologies to meet your
needs.
Solution
Design, develop, build, and
deploy the right solutions.
Deploy data integration
pipelines, data platforms,
BI reporting & analytics
solutions, and machine
learning models.
Deliver

Data Practice Services
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Information Strategy
• Power Alignment Facilitation
• Data Maturity Assessment
• Data Strategy & Roadmap
• Business & Technology Advisory
Consulting
Data Management Jumpstart
• Data Governance Jumpstart
• Data Stewardship Jumpstart
• Data Catalog Jumpstart
• Data Quality Enablement
• Modern Data Platform Evaluation
• Data Architecture Assessment
• Master Data Management Assessment
• Solution Architecture
• Data Architecture Design
• Cloud Data Platform Jumpstart
• Data Integration Development
Services
• Data Virtualization Jumpstart
BI & Analytics Jumpstart Services
• Dashboard Jumpstart
• Self-Service BI Jumpstart
• Data Science/Advanced Analytics
Enablement
BI & Analytics Acceleration & Enablement
• Dashboard & Report Services: Use
Case Definition, Design &
Development
• BI Tools Rationalization
• Self-Service CoE Enablement
• Machine Learning – POC, Model
development
BI & Analytics
Data Integration & Architecture
Strategic Data Management
“Trust Data” “Deliver Data” “Harvest Data”

Our proprietary Strategic Data Management &
Analytics (SDM&A) framework to help you
develop & accelerate strategies to achieve
maturity across the 7 Domains of Data
Management.
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Key differentiators

13
Strategic partners
More competencies
Data Partners &
product ecosystem
Strategic partner alliances and
competencies with market leaders and
market changers allow us to help you
execute on your strategy and identify
transformative opportunities to take
your business to the next level.

14

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About Denodo
OUR COMPANY
Data Management Leader
OUR PRODUCT
Leading Data Integration, Management, and Delivery Platform
OUR APPROACH
Logical First (Powered by Data Virtualization)
OUR USE CASES
Hybrid/Multi-Cloud Data Integration, Self-Service BI, Data
Science, Enterprise Data Services, Data Fabric, Data Mesh

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Long focus in data integration, management, delivery – since 1999
Denodo: Leader in Data Management
DENODO OFFICES, EMPLOYEES
Global presence – 25 offices in 20
countries; 500+ employees.
New offices in 2021 – Netherlands,
Belgium, Sweden, South Korea.
CUSTOMERS and PARTNERS
1000+ customers, including many F500 and
G2000 companies across every major industry.
300+ active and engaged partners, worldwide.
FINANCIALS
~50% annual growth
108% Net Retention; 4% Churn
$0 debt; Profitable
Leader: Gartner Magic Quadrant for
Data Integration Tools, 2021
Leader: Forrester 2022 Wave –
Enterprise Data Fabric, Q2 2022
Leader: Forrester 2017 Wave –
Data Virtualization, Q4 2017
LEADERSHIP
Customers’ Choice: 2022 Gartner Peer
Insights for Data Integration Tools
(2nd year in a row)

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▪ Data Virtualization is a technology which abstracts data consumers from where
data is located and how it is represented in the source systems.
▪ It allows building a business semantic layer on top of multiple distributed data
sources of any type without the requirement of replicating data into a central
repository.
▪ This semantic layer can be accessed in a secure and governed manner by
consumers using a variety of access methods such as SQL, REST, OData,
GraphQL or MDX.
▪ It’s the foundation for distributed and logical architectures
What is Data Virtualization

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Denodo Platform: ONE Logical Platform for All Your Data
Logically Integrate, Manage, Monitor; and Deliver Distributed Data
ANY DATA
SOURCE
ANY DATA
CONSUMER
Data
Governance
Tools
BI Dashboard
Report and Tools
Data Science &
Machine Learning
Apps
Mobile &
Enterprise Apps
Microservices
Apps
DB, DW &
Data Lakes
Files
Cloud DB
& SaaS
Streaming
Data & IoT
Cube
Smart Query
Acceleration
AI/ML Recommendations
& Automation
Advanced Semantics
& Active Data
Catalog
Unified Security &
Governance
Logical Data
Abstraction
Real-Time Data
Integration
ANY PLATFORM ENVIRONMENT
On-Premises | Cloud | Multi-Location | Containerzed

21
What is a Data Fabric?
Data Fabric
Location
Customer
Products
Architecture design pattern that serves as an integrated layer of data over all available data assets.
▪ Continuous analytics over all metadata assets to provide actionable insights and recommendations on data management.
▪ Results in faster, more informed, and, in some cases, completely automated data access and sharing
▪ Strongly supported by both Gartner and Forrester
▪ Business centric relationships and terminology
Supplier

What is Data Mesh?
Distributed Ownership Paradigm proposed by the
consultant Zhamak Dehghani in 2019

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Data Mesh Concepts
Data Accessibility across Enterprise
• Eliminate data silos by making data accessible in unified fashion regardless of its origin
• Foster Self-Service culture by enabling all users to achieve their business goals
Data Sharing Culture
• Enable data sharing culture within your organization to optimize the value of the data assets
• Team work and collaboration made easier with accessible data, and elimination of IT hurdles
Domain Data Is Key
• Business owns and drives the data needs and requirements
• Domain data comes first, the Integration and Processing will follow
Distributed Ownership
• Flexible decentralization capable of aligning with all business needs.
• Distributed compute, store, and ownership of data assets ensures rapid adoption
Data as a product
• Turn the data into a product to be used internally, externally, or both
• Data is your most valuable asset, time to treat is as such

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• Lack of domain expertise in centralized data teams
▪ Centralized data teams are disconnected from the business
▪ Need to deal with data and business needs they may not understand
• Lack of flexibility of centralized data repositories
▪ Data infrastructure of big organizations is very diverse and changes frequently
▪ Modern analytics needs may be too diverse to be addressed by a single platform: one size
never fits all.
• Slow data provisioning and response to changes
▪ Extracting, ingesting and synchronizing data in the centralized platform is costly
▪ Centralized IT becomes a bottleneck
What Challenges is a Data Mesh Trying to Address?

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▪ To ensure that domains do not become isolated data silos, the data exposed
by the different domains must be:
▪ Easily discoverable
▪ Understandable
▪ Secured
▪ Usable by other domains
▪ The level of trust and quality of each dataset needs to be clear
▪ The processes and pipelines to generate the product (e.g. cleansing and
deduplication) are internal implementation details and hidden to consumers
Key Concept: Data as a Product

Enabling a Data Mesh with
Data Virtualization

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▪ Business guides, controls, and
owns domain-centric data
▪ Virtual Data Fabric enabled
decentralized architecture
▪ Data Interfaces and Unified Data
Sharing Platform
▪ Enables Self-Services & Data
sharing culture
▪ Scalable, adoptable, and
responsive
Break technology silos, while keeping data ownership at the domain level
Data Mesh Concepts with Data Virtualization
Data Virtualization - Logical Data Fabric - Data Share Framework
Partner Data
Business Domains
Corporate Data External Data
Data Virtualization

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Data Virtualization for Data Mesh: Data Product Creation
With a Web-based Design Studio, abstract data sources of any format and location into a business friendly and optimized data asset for
streamlined consumption and creation of data product
▪ All data assets accessible as relational models
regardless of the nature of origin
▪ Metadata driven with zero data replication,
unless required by the use-case
▪ Business driven semantics layer
▪ Top-down or bottom-Up approach
▪ Real-time on demand data access
▪ Robust query optimization with
▪ Caching, MPP, Remote tables
▪ Cost-based optimizations
▪ Smart Query acceleration
▪ Query push-down, and others…

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Data Virtualization for Data Mesh: Data Product Creation
With a Web-based Design Studio, abstract data sources of any format and location into a business friendly and optimized data asset for
streamlined consumption and creation of data product
▪ All data assets accessible as relational models
regardless of the nature of origin
▪ Metadata driven with zero data replication,
unless required by the use-case
▪ Business driven semantics layer
▪ Top-down or bottom-Up approach
▪ Real-time on demand data access
▪ Robust query optimization with
▪ Caching, MPP, Remote tables
▪ Cost-based optimizations
▪ Smart Query acceleration
▪ Query push-down, and others…

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Data Virtualization for Data Mesh: Data Services
Enables a single point of access for all consumers, self-service, and applications to access the data assets via a business driven
semantics layer
▪ Native Denodo connectors in major BI tools such
as Tableau, MicroStrategy, Cognos, PowerBI, etc.
▪ Multiprotocol support including JDBC/ODBC,
OData, SOAP/REST/GraphQL
▪ Human or machine consumption via
XML/JSON/HTML
▪ Enables Self-Service applications and
microservices
▪ Single source of truth across multiple consumers
▪ Centralized, secure, and governed access
▪ Integrated notebook for data scientist
Cache
DATA VIRTUALIZATION
Cloud Data
Lake
EDW
Application
Database

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Data Virtualization for Data Mesh: Self-Service capabilities
Enterprise-wide directory of data products available for consumption for business users, developers, data scientists, and data stewards
▪ Discover and document data products across your enterprise, with AI/ML driven recommendations
▪ Graphical Query & Smart Auto-complete enables quick query creation & customization
▪ Integrated Delivery layer, ensures on-demand data access with full Data Lineage
▪ Secure and audited data access
▪ Statistics on data product use
▪ Team Collaboration Features
▪ Integration with external tools
▪ Different roles for catalog access

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Data Virtualization for Data Mesh: Self-Service capabilities
Enterprise-wide directory of data products available for consumption for business users, developers, data scientists, and data stewards
▪ Discover and document data products across your enterprise, with AI/ML driven recommendations
▪ Graphical Query & Smart Auto-complete enables quick query creation & customization
▪ Integrated Delivery layer, ensures on-demand data access with full Data Lineage
▪ Secure and audited data access
▪ Statistics on data product use
▪ Team Collaboration Features
▪ Integration with external tools
▪ Different roles for catalog access

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Data Virtualization for Data Mesh: Operations and Management
Solution Manager enables streamlined deployment for on premise, cloud, container, or hybrid architectures which are key to a
distributed ecosystem.
▪ Centralized Solution Manager provides for management and monitoring across all Denodo environments, while ensuring a secure access
for various personas
▪ Designed for the hybrid deployment, it can facilitate seamless cloud migration
▪ Diagnostics & Monitoring
▪ Scalable and Secure
▪ Deployment Lifecycle
▪ Automatic AWS/Azure deployment

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Data Virtualization for Data Mesh: Operations and Management
Solution Manager enables streamlined deployment for on premise, cloud, container, or hybrid architectures which are key to a
distributed ecosystem.

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Conclusions
• Data Mesh is a new paradigm for data management and analytics
▪ It shifts responsibilities towards domains and their data products
▪ Trying to reduce bottlenecks, improve speed, and guarantee quality
• Data lakes alone fail to provide all the pieces required for this shift
• Data Virtualization tools like Denodo offer a solid foundation for Data Mesh
▪ Easy learning curve so that domains can use it
▪ Can leverage domain infrastructure or direct them towards a centralize repository
▪ Simple yet advanced graphical modeling tools to define new products
▪ Full governance and security controls

August 12, 2022
Building the case for
Data Virtualization
Presented by Mike Mappes
Senior Strategic Data Management & Analytics Consultant

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Value Proposition with Data Virtualization
1. Zero replication, zero relocation – No physical movement or
data integration of data required to make it useful
2. Location-agnostic architecture – Hide the complexity of multi-
cloud, hybrid environments
3. Data is abstracted – Data and relationships are represented
logically as defined by the business rather than physically as
it exists across the ecosystem.
4. Faster time to market – Direct connectivity to system-of-
record data as it is produced and updated
5. Faster enablement of self-service – Access to broad range of
data to support business-specific needs and workflows
6. Centralized metadata, security and governance – Integrated
view of all data allowing for standardization and enforcement
of core principles of access, understanding and use

Modern Data Platform – Reference Architecture
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Approach
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The collaborative and interactive 2-3 hour workshop, involving business and technical
stakeholders, is organized around three discussion topics:
Analysis & Information
Gathering
• Gain understanding of key
business & technical factors
leading to interest in data
virtualization or integration
platforms
• Identifying constraints,
limitations and pain points
with current architecture
Problem Statement &
Recommendations
• Capturing use cases for
integration solutions
• Understand how virtualization
addresses use cases and
integrates with architecture
• Discuss recommendations on
data virtualization and data
management based on
discussion findings
Next Steps & Roadmap
• Identify next steps for proving
and showcasing data
virtualization; Proof of Value,
Pilot, specific use cases for
value & validation
• Potential roadmap for an
implementation approach

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Questions?

42
Thank you!
[Article] Deep Dive on Data Virtualization Use
cases
[Get aligned] Data Virtualization Discovery
Workshop
[Explore] Fusion Data Consulting Services
[Learn more] Fusion’s Partnership with Denodo
Additional resources
Saj Patel
Vice President, Data Practice
sajid.patel@fusionalliance.com
Mike Mappes
Senior Strategic Data Management & Analytics Consultant
mmappes@fusionalliance.com
Get in touch