2. Agenda
1. Introduction to Data Driven Everything on AWS
2. Challenges with Data Driven Cloud Modernization
3. Addressing Challenges with Denodo Platform for AWS
4. Denodo Platform Use Cases and Data Architectures.
5. Key Takeaways | Q&A

3. 3
Modernizing leads to maximum innovation velocity and optimal
total cost of ownership
On-premises Lift
and shift
Move to managed
databases
Modernize with
purpose-built
databases
Innovation
velocity
Total
cost of
ownership
(TCO)
Break-free from
legacy databases

4. 4

5. 5

6. 6
What are customers building?
Backup &
restore
Non-disruptive
Easy place to start
Integrated with all
major vendors
Archive &
compliance
Media workflows
Tape replacement
Public Sector,
FinServ,
Healthcare/Life
Sciences
Home
directories
Simple to move
Not sensitive to
latency
Significant cost
savings
Data lakes
Variety of analytics
tools
Built for
streaming data
Data visualization
Business-
critical
applications
Integrated with
major vendors
Fully managed
infrastructure
Lift-and-shift
migrations

7. 7
AWS Customer- Analytics Challenges in a Distributed Data Landscape
Point-to-point data integration approaches are
challenging:
§ Extracting and moving data increases latency
and cost, and decreases quality, thus lacking
unified data access
§ Every project solves data access and
integration in a different way, increasing IT
dependency
§ Solutions are tightly coupled to data sources,
impacting flexibility, agility and overall
governance
DATA
SOURCE
DATA
CONSUMER
Data
Governance
Tools
DB, DW &
Data Lakes
Files
BI Dashboard
Report and Tools
Data Science &
Machine Learning
Apps
Mobile &
Enterprise Apps
Microservices
Apps
Cloud DB
& SaaS
Streaming
Data & IoT
Cube

8. 8
• The business wants more useful data
• Timely, curated, usable
• IT can’t keep up
• 67% of companies use less than half
of their data*
• IT stuck in old school thinking about data
management
• ‘Business as usual’
The ‘Useful Data’ Gap
* Source: Denodo Global Cloud Survey 2022

9. 9
AWS Analytics Data Strategy, Keynote
Remember, re:Invent 2022

10. 10
10
Businesses need a new approach to
connect data silos in real-time to
support various applications, insights,
and analytics.

11. 11
Modern Data Architectural Patterns & Data Driven Analytics
Data Mesh
Data Lakehouse
Data Lake
Data Fabric
Cloud Data Warehouse

12. 12
Real World Data Lake Example – AWS
Trusted Data Zone
Raw Data Zone Refined Data Zone
Transformation Transformation Data Consumers
Networking, Infrastructure & Security
Data Ingestion
Data
Sources
Data Catalog and Search – Asset Registry Workflow Orchestration, DevOps and CI/CD

13. 13
Denodo + AWS – Simple and Complementary Recipe!
• Embrace distributed data landscape
• Embrace the fact that data resides in multiple
locations or systems – on-prem, hybrid, multi-
cloud. All data needs to be managed with
consistency
• Use a Logical approach to manage it
• Consumers access data through a centralized
semantic model, decoupled from data location
and physical schemas, that can enforce security
and governance requirements

14. 14
Denodo Platform: ONE Logical Platform for All Your Data
Ease of Use Fast Query
Response
Integrated,
Active Data Catalog
Universal
Connectivity
Modern Data
Services API Layer
Dynamic Data
Masking
Automated Cloud
Management
Key Differentiators
83% reduction
in time-to-revenue
67% reduction
in data preparation effort
65% decrease
in delivery times over ETL
Source: Forrester Total Economic ImpactTM of Data
Virtualization, 2021
Hybrid/
Multi-Cloud
Security &
Governance
Al/ML
Recommendations
Advanced
Semantics
Data Catalog
Discover / Explore /
Document
BI Tools
SQL / MDX
Data Science
Tools
Data as a Service
RESTful / Odata
GraphQL/ GeoJSON
Files
Cubes
Cloud
Stores
Traditional
DB & DW
INTEGRATE
MANAGE
DELIVER
Disparate data in
any location, format
or latency
Related data with a universal
semantic model and AI / ML
functionality enabling vital
data governance
And democratize data using
BI & data science tools,
data catalogs, and APIs
Data Lake &
NoSQL
Query
Optimization &
Acceleration

15. On-
Premise
Transition
to Cloud
Hybrid
Multi-
Cloud
Stages of the Cloud Journey

16. •
•
•
•
•
•
•
Transition (or Migration) to the Cloud Challenges

17. 17
Reduce the Business Impact
1 - Transition to the AWS Cloud (Minimize Business Disruption)
Business Need
§ Transition to cloud – migrate
EDW
§ Real-time analytics from Business
Users and Data Scientists
§ Security and governance across
multiple analytical tools need to be
centralized
§ Acts as a single semantic layer
§ Homogeneous data access regardless of
back-end technology
§ No need to deal with new languages and
APIs: access to SFDC, Excel, Amazon
Redshift, Oracle, Hadoop, other SaaS
APIs, etc.
§ Consistent business data model across all
consumers and reporting tools
§ Reusability of analytical objects across
multiple tools and consuming applications
§ Abstracts access to disparate data sources
§ Change in the data sources buffered
minimizing the impact on consumer
business applications
§ New technology adoption with minimal
impact on the business
§ Minimizes impact on consumers
§ Minimizes cross-environment connectivity
§ reducing risks of unauthorized access to
data
§ Amazon Athena
§ Amazon S3 Buckets
§ Amazon Redshift
§ Amazon Aurora
§ AWS PaaS - RDBMs
Denodo AWS

18. 18
Transition to Cloud | Cloud Migration Acceleration
Denodo becomes the common access layer for all on-
premise and cloud systems:
Access to all data from a single system
The data can be accessed directly from the
original system, without the need for replication
The data can still be easily replicated and hidden
if necessary
Simplify data aggregation, regardless of the location or
format of the data
Allows semantic models definition, independent of
the original formats and structures
Advanced security for all data
Documentation and usage statistics included in the
Data Catalog

19. •
•
•
•
Hybrid (Cloud and On-Premise) Data Integration – Customer 360 / Single View

20. 20
AWS Cloud Modernization - LeasePlan Data Hub Architecture
`
DATA
ACQUISITION
DATA
SOURCES
DATA
STORE (RAW)
ANALYTICS
WAREHOUSE
DATA
SCIENCE
DATA
FABRIC
DATA
CONSUMER
Next Gen Data Management (Meta-data, data quality, governance)
Meta data management, data quality, data governance as central components guarding the overall
data-asset of the corporation to allow trusted access to data for utilisation across the enterprise
Structured
Unstructured
ETL/ELT
ORCHESTRATION
STREAMING
Native Extraction
No ETL Tool(s)
AWS
Kinesis
Airflow
SAP BW/4HANA +
HANA Native
Raw
Quality
Integration
Consumption
Glacier
Archive
BW/4HANA +
HANA Native
NG Finance 1
NG Insurance
NG Procurement
NG Marketing
NG Sales
NG Service
NG Commerce
NG Fleet Ops
NG Supplier
Engagement
NG Policy Mgt.
NG Portals
NG Contact Center
Legacy – NOLS/
DB2/AS400 etc.
Other External Data:
Telematics, IoT, GA,
Social feeds,
streams
Analytics for
Cloud
Analysis for Office
AWS
SageMaker
Power BI
Role Based Access
Control
Caching

21. 21
Take the right decision on accurate data
2 - Real-Time Analytics for Business Users
Business Need
§ Transition to cloud – migrate EDW
§ Real-time analysis from
Business Users and Data
Scientists
§ Security and governance across
multiple analytical tools need to be
centralized
§ Enables Self-Service BI
§ IT delivers a governed layer of “business
views” to business users
§ Business users can generate any report
over those IT-governed business views
§ Business views can be adapted for every
type of user making use of the same
terminology and naming conventions for
every Line of Business
§ Incorporate geospatial, IoT, and
other streaming data, to enable
real-time data services
§ Accelerate cloud analytics with Amazon’s
elastic infrastructure (EC2, auto-scaling)
§ Data is immediately available for use
without delays
§ Integrate and Manage data across
Amazon Redshift, Amazon RDS,
Amazon S3 in real-time to drive
advanced analytics
§ Source data to Amazon Lambda
serverless processes and expose them
as data source for BI-Analytics
§ Visualize data and reports in real time
with QuickSights
Denodo AWS

22. 22
How Does Denodo Platform 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

23. 23
FAA – Federal Aviation Administration – Streamline Operations/Analytics
ü Reduced the IT Operations Cost by 99.8%,
while accelerating data access by 96%.
ü To reduce costs and streamline IT operations,
the U.S. Federal Aviation Administration (FAA)
wanted to consolidate multiple IT
organizations – each supporting different
mission areas – into a single office reporting
to a single CIO.
FAA leveraged the Denodo platform on AWS to:

24. 24
Across multiple analytical tools
3 - Centralized Security and Governance
Business Need
§ Transition to cloud – migrate EDW
§ Real-time analytics from Business
Users and Data Scientists
§ Security and governance across
multiple analytical tools need to
be centralized
§ Unified Security Layer
§ Global Tag-based Policy Engine
§ Role-based authorization to all tables in
the virtual layer (RBAC)
§ Attribute-based access control (ABAC)
§ Security is moved outside the reporting
layer to avoid security bypasses
§ Centralized access point simplifies
operations and auditing
§ Data Masking / Obfuscation
§ Centralized Governance Layer
§ Centralized metadata catalog accessible
for both technical and business users
§ Data Source refresh, change impact
analysis, full data lineage, etc.
§ Protects data sources from uncontrolled
access through query throttling, limiting
#concurrent queries over them, limiting
resulting datasets sizes, enabling the cache
for minimizing the access to data sources for
some views, etc.
Denodo AWS Services
§ Datawarehouse Built for the cloud
§ Athena
§ Redshift
§ Secured, Managed Access
§ With Amazon Resource Manager
§ Identity Management & SSO Amazon
IAM

25. 25
Data Fabric Overview
Core Principles:
ü Data Integration
ü Data Governance
ü Data Democratization
ü Data Intelligence
ü Data Interoperability

26. 26
Data Mesh Powered by Denodo Data Virtualization
SQL
Operational EDW
Data Lakes Files
SaaS APIs
REST GraphQL OData
Event
Product
Customer Location Employee
Common Domain Event Management Human Resources
MDX
2.Domains connect
their data sources
❷
1.Each domain is given a
separate virtual schema.
A common domain may be
useful to centralized data
products common across
domains
❶
3.Metadata is mapped
to relational views.
No data is replicated
❸
4.Domains SMEs can
model their Data
Products.
Products can be used to
define other products
❹
5.For execution, Products can
be served directly from
their sources, or replicated
to a central location, like a
lake
❺
6.A central team can
set guidelines and
governance to
ensure
interoperability
❻
7.Products can be access via
SQL, MDX or exposed as an
API. No coding is required
❼ 8.Infrastructure can
easily scale out in a
cluster
❽
New architectural paradigm for data management | distributed organizational paradigm | Domains in charge of Data Products

27. 27
Data Fabric & Data Mesh Powered by Data Virtualization

28. Summary and Takeaways
Benefits of Logical Data Architectures

29. Benefits of a Logical Data Architecture
“Now, we can do weekly releases.
We’re able to add new data sources
within 2 to 3 hours. We’re about 60%
faster than we were in the old world.”
VP of data and analytics, real estate
“To me, it all boils down to speed to
insights. Not having to wait to get the
question that you have top-of-mind
answered with data is huge.”
VP of data and analytics, real estate
29

30. 30
Try Denodo Platform on AWS – Get Started Today!
• 30 days Free Trial of Denodo Professional via AWS Marketplace
• AWS Marketplace Transactable Pay-Go/Private Offers
• Denodo – AWS Test Drives (free hands-on learning in 2 hours) :
Denodo-AWS BI
Denodo-AWS Data Science
Visit Denodo Platform and AWS
https://www.denodo.com/en/denodo-platform/denodo-platform-for-aws

31. Q&A

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