1. WEBINAR
Sylvie Baes
Partner & Business Development,
Inforoad
Vincent Fages-Gouyou
EMEA Product Management Director,
Denodo
Cloud Migration headache?
Ease the pain with
Data Virtualization!

2. Please contact
mark.verstuyft@inforoad.be /
sylvie.baes@inforoad.be
What do we do?
§ InfoRoad delivers professional services in Analytics and Data integration
with a specific focus on Data Virtualization with Denodo.
The services InfoRoad offers are:
§ Denodo Tool Implementations
§ Support of Proof-of-concepts
§ Architecture Advice
§ Denodo Adoption Service
§ Denodo Reselling
Typical use cases we solve
§ 360° view of the customer
§ Self-service analytics
§ Real time analytics
§ Centralized data governance
§ Big data and data lake governance
§ Integration of structured and unstructured data
§ Enabling migration to/integration in the cloud
§ …
Some of our customers:
CONNECT, COMBINE & CONSUME
Sales
HR
Execu
tive
Marketi
ng
Apps/
API
Data
Science
AI/ML
A Cronos Group company

3. Agenda
1. Drivers for moving to the cloud
2. Why cloud migration is a headache?
3. Enterprise data delivery headache
4. The role of Data Virtualization in the cloud journey
5. Data Virtualization patterns in cloud architectures
6. Demo
7. Q&A

4. 4
Motivations for the Transition to Cloud
New Capabilities and Reduced Cost
§ Lower cost of operations (automation)
§ No up-front HW investment
§ Access from anywhere
§ Flexibility to upgrade capacity
§ Less dependency on desktop software
§ Ease data sharing & monetization

5. 5
A (Typical) Journey to the Cloud
On-Premise + SaaS
+ Cloud
On-Premise + SaaS
On-Premise
Cloud Native
On-Premise + SaaS
+ Multi-Cloud

6. 6
Approaches to Cloud Transition
1. Rehost
§ Move data as is to the same system, but hosted in a cloud provider
§ For example, from on-prem Oracle to Oracle RDS in AWS
2. Replatform
§ Move data as is to a new cloud-native system
§ For example, from an on-prem Teradata EDW to Snowflake
3. Refactor
§ Move to a new cloud0native system, but taking the opportunity to also change schemas,
data structure, ingestion and reporting tools, etc.
§ Changes not just in the systems, but in the data strategy itself

7. 7
Why cloud migration is a headache?
Misestimate Risks
§ Lengthy migration (~15 months)
§ Sovereignty
§ Costs control
§ Regulatory compliance
§ Re-development, drop of productivity
New challenges
§ Security
§ Network latency
§ Minimizing vendor lock-in risk
§ Existing challenges in data management still apply!

8. 8
The Data Strategy as part of the Transition to the Cloud
§ A complete change in architecture is a complex process,
that modifies multiple elements in the data ecosystem
§ However, it guarantees that the data strategy evolves and
follows new trends
§ It’s not just a change in RDBMS vendor
§ It addresses existing challenges and limitations of the existing
data strategy
§ A change of this caliber implies longer projects with
intermediate stages
§ It can last years
§ It involves intermediate (or permanent) hybrid states, where
cloud and on-prem systems coexists

9. 9
What Pieces are Involved in a Strategy Change?
§ Adoption of cloud-based SW solutions
§ AWS, Azure, Google offer cloud alternatives for most common software
§ Some companies have developed specialized cloud-based solutions, like Snowflake, Databricks, Looker
§ Traditional on-prem software has been adapted to the needs and requirements of cloud deployments, like
Tableau and Denodo
§ Adoption of new approaches to data management (e.g. Data lakes, streaming) that adapt to new
trends and requirements (predictive analytics, machine learning, etc.)
§ Migrate to SaaS options for packaged applications
§ For example, migrating from an on-prem CRM to Salesforce.com, marketing tolos, etc.
§ Extended use of web APIs for application-to-application communication
§ New authentication and authorization systems based on Identify Provides (SAML, OAuth, OpenID,
etc.)
§ And many more

10. 12
Enterprise’s Data Delivery Architecture
Data Science
Data Quality ML / AI
Locations
Data Sources
OLAP
Visualisation

11. 13
Enterprise’s Data Delivery Architecture
Data Science
Data Quality ML / AI
Locations
Data Sources
OLAP
Visualisation
Governance, Metadata Management, Data Mart
Security
Data Access
Data Virtualization Data Services

12. 14
Enterprise’s Data Delivery Architecture
Federation
Transformation
Abstraction
Data Service Dynamic Query
Optimization
Cost Based
Optimizer
Query
Rewriting
Caching MPP
Security &
Governance
Lifecycle
Management
Data Catalog
Discover
Collaborate
Query
Categorize

13. How Data Virtualization can help
with transitioning workloads to
the Cloud

14. 16
The Value of a Data Delivery layer
§ 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
§ 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

15. 17
As a Global Strategy
For the first time, a technology allows you to
define and implement a data delivery strategy
§ Independent from the sources where you
store and process your data
§ Independent from the consuming
applications
§ Independent from the location of the
deployment
§ Can enforce security and access policies
§ Provides strong governance management

16. 18
Avoid expensive Cloud ‘data egress’ charges
§ Public cloud providers charge every time you move data from their cloud storage to your on-
premises storage. These Egress fees can add up. But no charges for Ingress
§ Transfer within the same regions or availability zones (AZ) is free
§ For some services, the cost for moving data in / out is accounted for in the cost of the
service itself, rather than billed as a separate data transfer fee.
Recommendations:
§ Keep Denodo server closer to the data sources (minimize data movement)
§ Multi-Location architecture helps with faster integration.
§ Ability to cache your active data on-premises, thus avoiding Cloud data Egress fees.

17. Data Virtualization patterns in
cloud architectures

18. 20
Use Case: Virtualizing to Accelerate Data Integration
DV becomes the common Access layer for both on-
rem and cloud systems:
§ Access to all data from a single system
§ Data can be accessed straight from the original
systems, without the need for an additional copy
§ Data can be easily replicated and cached if
necessary
§ Simplifies the combination of data, regardless of
original format and location
§ Enables the definition of semantic models,
independant from original formats and structures
§ Adds advanced security settings to all data
§ Documentación y estadísticas de uso incluidas en el
Denodo Data Catalog

19. 21
Use Case: Virtualizing to simplify migrations
§ Migrations of key systems are complex
§ Normally involve multiple phases
§ Can last months or years
§ Data Virtualization, thanks to the
decoupling and abstraction capabilities,
simplifies the process:
§ Shields the consumers from changes in
the backend
§ Allows IT to move data from one system
to the other without changes in the
consuming applications

20. 22
Use Case: Virtualizing to Acceleart and Reduce Cost
§ Data Sources charges based on usage or data volumes. For example:
§ Snowflake charges by “compute credits”
§ Athena by bytes scanned
§ Smaller summaries mean less data processed and less CPU time
§ Summarized queries are not just faster, but also cheaper
§ Until now, these technologies were only available in some reporting tools
(BO, Microstrategy) and on-prem EDWs (Oracle, Teradata)
§ No cloud-based RDBMS includes these capabilities
§ Denodo supports aggregate-aware query acceleration regardless of consuming
tools and source capabilities
§ For more details:
https://www.denodo.com/en/webinar/accelerate-your-queries-data-virtualization
SALES
10 billion rows
Sales summary
1 million rows

21. 23
Capabilities: Is Denodo ready for the Cloud?
Denodo 8 has been re-designed as a cloud-native platform!
§ Native Deployment Options
§ Automated, web-based management of clusters, instances,
autoscaling, load balancing, etc.
§ Servers run on customer account, for security and latency reasons
§ Available for AWS, coming for Azure
§ Tight integration with cloud ecosystems:
§ Snowflake, Redshift, BigQuery, Synapse, Athena, and many others
§ SSO and popular IdPs like AWS, Azure, Okta, Ping, Duo, etc.
§ Web-based client for development, monitoring, management,
etc.
§ Available in cloud marketplaces for AWS, Azure and GCP with
attractive play-as-you-go options

22. 24
Demo
Cloud Migration & Data Privacy

23. 25
Cloud Sync.
Cloud Migration & Data Sharing
Clients
Forex
§ Multiple Sources & APIs
§ Data Linage / Governance
§ Federated Queries
§ Data access protection
§ API Data service provider
§ Cloud Data Warehouse
Portfolios
Details
Quote
Mask

24. 26
Cloud Migration
DATA DELIVERY
RDMS
SAS API
API
i-portfolio
i-contact_portfolio_full
Views
Interfaces
Remote Tables
contact_portfolio_full
SQL
rt-contact_portfolio_full
Data Migration
Remote Table
Synchronization

25. Q&A

26. Thank You!