Watch full webinar here: https://bit.ly/3FcgiyK Denodo recently released the Denodo Cloud Survey 2021. Learn about some of the insights we have from the survey as well as some of the use cases Denodo comes across in the cloud. We will also conduct a brief product demonstration highlighting how easy it is to migrate to the cloud and support access to data in hybrid cloud architectures. In this session not only will we look at what you, the customers are saying in the Denodo Cloud Survey but also: - We will explore how, in reality, many organizations are already operating in a hybrid or multi-cloud environment and how their needs are being met through the use of a logical data fabric and data virtualization - We will discuss how easy it is to reduce the risk and minimize disruption when migrating to the cloud - We will educate you on why a uniform security layer removes regulatory risk in data governance. - Finally we will demonstrate some of the key capabilities of the Denodo Platform to support the above.

1. EMEA WEBINARS
Enabling Cloud Data
Integration

2. Speakers
Robin Tandon
Product Marketing Director
Denodo
Sales Engineering Director
Denodo
Mark Pritchard

3. Agenda
1. Key Findings from the 2021 Denodo Cloud Survey
2. Cloud Data Integration, The Journey
3. Denodo Demonstration
4. Case Studies
5. Q & A

4. Key Findings from the Denodo
Cloud Survey 2021

5. 5
Cloud adoption maturity is on the
rise, with close to 80% of those
surveyed already running some
kind of workload in the cloud.

6. 6
25% growth in the number of
advanced users clearly indicates
an upward trend in cloud usage
and adoption.

7. 7
Close to 25% of participants
indicated concerns over the
limited cloud skills and ability to
manage workload deployments in
a multi-cloud environment.

8. 8
Hybrid cloud has consistently been the
top choice over the past years as
organizations migrate workloads to the
cloud. More than one-third (36%) of
participants are leveraging hybrid cloud
while private cloud is still the go-to
deployment model for privacy-bound
applications or those that safeguard
mission critical operations.

9. 9
Microsoft Azure, for the second time
in a row, has come out as the lead
cloud provider, with 34% of
participants, with Amazon right
behind it. While there could be
regional influences, the survey has
shown that the EMEA region
has a slightly higher preference for
Azure compared to the other
cloud providers.

10. 10
BI and data integration are the
top cloud use cases
BI and analytics remain the top cloud
initiative, while establishing better
data integration and expanding data
science using AI/ML are tied as the
runner up.

11. 11
Close to 50% of participants leverage
multiple solutions such as data lakes and
data warehouses for data management in
the cloud.
Data virtualization can nicely support data
infrastructure modernization while providing
core capabilities such as data catalogs and
support for handling streams.
Demand for data integration in
the cloud is driven by data lakes
and cloud data warehouses!

12. 12
ML/AI and streams processing services
see big jumps in usage, close to 50%,
while infrastructure usage
demonstrates fairly good consumption
after analytics.

13. 13
Close to 25% of participants indicated
concerns over the limited cloud skills
and ability to manage workload
deployments in a multi-cloud
environment.

14. 14
45% of our participants see a strong
value in leveraging enterprise
agreements via marketplaces to close
deals faster, and a similar percentage
value discount programs, which help
exploit budgets from a procurement
perspective.

15. 15

16. 16

17. Cloud Data integration – The
Journey

18. 18
It’s Not “If” or “When”, rather How best to Optimize the journey !
Migrating workloads to cloud ?

19. 19
Understanding Cloud Migration
PUBLIC CLOUD
Move data or
applications or both
from on-premises to
Public cloud
HYBRID CLOUD
Move partial
workloads to the
cloud, with some
still on premises
MULTI-CLOUD
Migration of on-
prem apps /data to
multiple public
clouds (use case
driven)
PRIVATE CLOUD
Build an on-premises
cloud computing
platform

20. 20
Common Migration Methods from On-Premises to Public Cloud
v

21. 21
Denodo Platform 8.0 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
175+
data
adapters
Cloud
Stores
Hadoop
& NoSQL OLAP Files Apps Streaming SaaS

22. 22
A logical data layer – a “data fabric” – that provides high-performant, real-time, and secure access to
integrated business views of disparate data across the enterprise
The Denodo Platform
• Data Abstraction: decoupling
applications/data usage from data
sources
• Data Integration without replication
or relocation of physical data
• Easy Access to Any Data, high
performant and real-time/ right-
time
• Data Catalog for self-service data
services and easy discovery
• Unified metadata, security &
governance across all data assets
• Data Delivery in any format with
intelligent query optimization that
leverages new and existing
physical data platforms

23. 23
Denodo Data Catalog
Data Catalog within Data Virtualization to seamlessly
integrate data catalog and data delivery
Dynamic Catalog of curated, timely, contextual, and reusable
information assets and data services.
Govern – Fine-grained privilege that governs access to the catalog;
both metadata and information assets.
Describe – Ability to describe data assets with categorization,
tagging, annotations, lineage and other business-oriented metadata.
Usage-based metadata – who, when, what, why, and how of data
consumption.
Lightweight Data Preparation – Ability to transform, refine, and
customize data assets for business use.
Enhanced UI – Business-friendly user interface geared to roles such
as data stewards, data analysts, and citizen analysts

24. 24
But There are Challenges with Cloud Adoption/Migration
• Silos remain (Cost and Interoperability).
Lack of strategy.
• Security concerns in the Cloud (GDPR …)
• Performance bottlenecks (data across
regions, infrastructure)
• Business downtime (complexity of
migration, apps/data sources)
• Learning new skills and resources.
8

25. 25
Stages of the Cloud Journey
All systems are on-premise.
Using traditional databases,
etc. – maybe an on-premise
Hadoop cluster. Lots of ETL
pipelines. Using Denodo for
integrated view of data.
Systems are now on-premise and in the Cloud –
initially hosted by the preferred Cloud provider. The
data is balanced across the different environments
although the bulk of the data is initially on-premise.
ETL-style data movement is often used to move data
from on-premise systems to Cloud-based analytical
systems. The systems are more complex and users
need to be able to find and access data from on-
premise and Cloud locations.
In reality, this is a hybrid/multi-Cloud
environment, with systems in multiple
Clouds (AWS, Azure, GCP, Salesforce, etc.)
and a few legacy systems still on-premise.
The environment is even more complex as
workloads can move between Cloud
providers to take advantage of new
capabilities, cost optimization, etc. Users
still need to find and access data in this
environment.
System modernization initiatives move
applications and data to the Cloud. For critical
systems, this migration is typically a phased
approach over a period of months (or years).
(Note: Most organizations skip this stage and go straight to
multi-cloud)
Systems have moved to the Cloud (although some systems
are still on-premise and cannot be moved to the Cloud). The
‘center of gravity’ for data is solidly in the Cloud. More
processing and data integration occurs in the Cloud. Data is
moved from on-premise systems to the Cloud using ETL.
User data access is predominantly from Cloud systems.
On-Premise
Transition
to Cloud
Hybrid
Cloud
Single
Cloud
Multi-Cloud

26. 26
Stages of the Cloud Journey
All systems are on-premise.
Using traditional databases,
etc. – maybe an on-premise
Hadoop cluster. Lots of ETL
pipelines. Using Denodo for
integrated view of data.
Systems are now on-premise and in the Cloud –
initially hosted by the preferred Cloud provider. The
data is balanced across the different environments
although the bulk of the data is initially on-premise.
ETL-style data movement is often used to move data
from on-premise systems to Cloud-based analytical
systems. The systems are more complex and users
need to be able to find and access data from on-
premise and Cloud locations.
In reality, this is a hybrid/multi-Cloud
environment, with systems in multiple
Clouds (AWS, Azure, GCP, Salesforce, etc.)
and a few legacy systems still on-premise.
The environment is even more complex as
workloads can move between Cloud
providers to take advantage of new
capabilities, cost optimization, etc. Users
still need to find and access data in this
environment.
System modernization initiatives move
applications and data to the Cloud. For critical
systems, this migration is typically a phased
approach over a period of months (or years).
On-Premise
Transition
to Cloud
Hybrid
Cloud
Single
Cloud
Multi-Cloud
(Note: Most organizations skip this stage and go straight to
multi-cloud)
Systems have moved to the Cloud (although some systems
are still on-premise and cannot be moved to the Cloud). The
‘center of gravity’ for data is solidly in the Cloud. More
processing and data integration occurs in the Cloud. Data is
moved from on-premise systems to the Cloud using ETL.
User data access is predominantly from Cloud systems.
1 2 3

27. 27
Cloud Migration Options
Re-Host – ‘Lift and Shift’
Take existing data and copy
it to Cloud “as is” into same
database
▪ Good for smaller
data sets or data sets
with low importance
Re-Platform
Relocate to new database
running on Cloud – everything
else stays the same
▪ e.g. move from Oracle 12g to
Snowflake
Re-Factor/Re-Architect
Move to a different database
*and* change the data schema
▪ e.g. move from Oracle to
Redshift and re-factor data
model, partitioning, etc.

28. 28
Cloud Migration Using Data Virtualization
• Large or critical Cloud migrations are risky
• Big Bang approach is not advised
• Phased approach is recommended
• Select data set to migrate, copy to
Cloud
• Test and tune data access, then go
live
• Repeat for next data set and so on
• Use Denodo as abstraction layer during
migration process
• Isolate users from shift of data

29. 29
Hybrid Data Integration with a Logical Data Fabric
Common access point for both on-premise and
cloud sources
• Access to all sources as a single schema with
no replication: Virtual data lake
• Enables combination of data across sources,
regardless of nature and location
• Allows definition of common semantic model
• Single security model and single point of
enforcement
Active
Directory
Data Center
Cloud

30. 30
Multi-Cloud Integration with Logical Data Fabric
Amazon RDS,
Aurora
US East
Availability Zone
EMEA
Availability Zone
On-prem
data center

31. 31
Simplifying Cloud Architectures with Data Virtualization
Product Demo

32. 32
Scenario
Cloud Migration Demo
EDW
Amazon Redshift
Master Data
Amazon Aurora RDS
On-Premise Data Center
Store Sales Date
Legacy Master Data
Customer Profitability Report
Customer (Interface) Customer (Interface)
Abstraction Layer

33. DEMO

34. Customer Case Study

35. Business Need Solution Benefits
Global industrial real estate company creates a new data
architecture and successfully launches data analytics program for
cost optimization.
Case Study
• Create a single governed data access layer to
create reusable and consistent analytical assets
that could be used by the rest of the business
teams to run their own analytics.
• Save time for data scientists in finding ,
transforming and analysing data sets without
having to learn new skills and create on data
models that could be refreshed on demand.
• Efficiently maintain its new data architecture with
minimum downtime and configuration
management.
• The analytics team was able to create business
focussed subject areas with consistent data sets
that were 30% faster in speed to analytics.
• Denodo made it possible for Prologis to quick
start advanced analytics projects.
• Denodo deployment was as easy as a click of a
button with centralized configuration
management and easy to upgrade and scale up
and down according to the workload.
• Denodo was used to create a logical data
warehouse that made the data available for
analytics. Data Catalog feature used by data
scientists to find the data easily.
• Denodo was used to leverage the microservices
architecture to push enterprise data into the data
modals and then get the result set back into
Denodo to make them available for consumption.
• Terraform used to script a lot of configurations
that were running Denodo. CICD pipeline used to
automate the Denodo configuration backup.
35
Prologis is the largest industrial real estate company in the world, serving 5000 customers in over 20 countries and
USD 87 billion in assets under management. Prologis was ranked the top U.S.company and sixth overall among the
2019 Global 100 Most Sustainable Corporations in the World at the World Economic Forum in Davos.

36. 36
Old State Architecture
8 Database
12 Integration
5 Reporting
2 Virtual Desktop
27 Servers
4 Environments

37. 37
Current State Architecture
wc_monthly_occupancy_rpt_f wc_lease_amendment_d w_day_d wc_property_d
MARKET_AVAILABILITY_CURRENT MARKET_AVAILABILITY_FUTURE
Prologis
SnowFlake
API
Access
Informatica
Cloud
ShareHouse
ODBC JDBC
peoplesoft_gl_actuals yardi_unit_leasing p360_property
WAF
AWS Lambda APIs

38. Conclusions

39. 39
Data is a critical asset to any organization – and
aligning the right architecture is a fundamental
step.
Data virtualization is core to a data fabric and
accelerates a wide range of initiatives; from self-
service cloud analytics to data marketplaces to
regulatory reporting and compliance in the cloud.
Cloud data integration using data virtualization
enhances user productivity and time to value.
1
2
3
Key Takeaways

40. Q&A

41. 41
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42. Thanks!
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