Watch full webinar here: https://bit.ly/3CGBazj According to a recent Forrester survey, while 85% of organizations want to improve their use of data insights in their decision-making, 91% of the respondents report that they are not able to improve upon their data-driven decision making. The problem - organizations often turn to the data lakes, data lake houses, cloud data warehouses, etc. as their monolithic data repository, for all of their data and analytics needs. But the hard reality is that data is and will be spread across multiple repositories that extend across cloud and regional boundaries. Attend this webinar to gain key insights on why a logical data fabric powered by data virtualization is critical for any organization's data and analytics. Tune into this session & learn: - What is a logical data fabric and its key components - Why organizations are adopting a logical data fabric as opposed to a physical one - How it helps business users, data scientists, and other data consumers alike in data access, consumption, and data-driven decisions - How organizations are using logical data fabric for faster time-to-insights

1. DENODO LUNCH AND LEARN ASEAN
Logical Data Fabric Powered
by Data Virtualization:
An Overview
Katrina Briedis
Sr. Product Marketing Manager,
APAC

2. Agenda
DENODO LUNCH AND LEARN ASEAN
1. Need for a Data Fabric
2. What is a Data Fabric
3. Logical vs. Physical Data Fabric
4. Faster Time-to-Value with Logical Data Fabric
5. Successful Customer Use Case
6. Best Practices to Get Started
7. Q&A
8. Next Steps

3. DENODO LUNCH AND LEARN ASEAN
Need for a Data Fabric
Challenges

4. 4
Distributed Data Landscape
Point-to-point data integration approaches are
challenging:
§ Extracting and moving data increases latency
and cost, and decreases quality
§ Every project solves data access and
integration in a different way
§ Solutions are tightly coupled to data sources,
impacting flexibility and agility
§ No centralized governance and control
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

5. 5
Where Is My Data?
Web Content
Big Data Application
Graph Database
Relational
Databases
Cloud Storage
(S3, ADLS, GCS)
JSON/XML Files
SaaS Applications
Flat Files
Data Warehouse
Excel
Spreadsheets
?

6. DENODO LUNCH AND LEARN ASEAN
What is a Data Fabric?
From an Analyst Perspective

7. 7
A data fabric is an architecture pattern that informs and automates
the design, integration and deployment of data objects regardless
of deployment platforms and architectural approaches.
It utilizes continuous analytics and AI/ML over all metadata assets
to provide actionable insights and recommendations on data
management and integration design and deployment patterns.
This results in faster, informed and, in some cases, completely
automated data access and sharing.

8. 8
Pictorial View of a Data Fabric – from Gartner
In Layman Terms
• “Integrate data” from
disparate data sources
• Securely deliver an
“integrated view” of the
different data objects
• Consume the “integrated
data” for analytics and
operational purposes
• Automate the entire
process using AI/ML

9. 9
Data Fabric Key Elements – Gartner Definition
Data
Consumers
Data
Sources
Data Fabric

10. 10
By 2023, organizations utilizing data fabrics to dynamically connect, optimize and automate data
management processes will:
Reduce time to data delivery by 30% and
Automate manual transformations by 45%.
Gartner - 2019 Magic Quadrant for Data Integration tools
Gartner - 2020 Magic Quadrant for Data and Analytics Service Providers
By 2023, data fabric enabled automation in data management and integration will:
Reduce dependency for IT specialists by 20%
Reduce integration costs by 45%.
Gartner: Data Fabric Benefits

11. 11
Data Fabric Key Elements – Forrester Definition
Data
Consumers
Data
Sources
Data Fabric

12. 12
Forrester Total Economic ImpactTM of Data Virtualization Using the Denodo Platform, October 1, 2021
Forrester Total Economic ImpactTM of Data Virtualization

13. DENODO LUNCH AND LEARN ASEAN
Logical vs. Physical
Data Fabric

14. 14
Conflict Between Centralizing and Decentralizing Data
Stop collecting, Start connecting

15. 15
Gartner: Evolution from Physical to Logical Data Warehouse

16. 16
Demerits: Monolithic Architecture
Monolithic Architecture
• Monolithic: Physically centralize data in a single
location
(e.g. data lake/house)
Challenges (one size never fits all)
• Takes time & effort: intensive data replication for
each new data need
• Difficult to maintain: changes require modifying
pipelines and datasets
• Incompatible: Existing analytical systems cannot be
reused; need to ingest all data into a new system
…for Data Integration and Management
Stop collecting.
“Inherent in the LDW architecture is the recognition that a
single data persistence tier and type of processing is
inadequate to meet the full scope of modern data and
analytics demands.”
– Gartner: The Practical Logical Data Warehouse, Dec 2020, by
Henry Cook, Rick Greenwald, and Adam Ronthal

17. 17
…for Data Integration and Management
Merits: Logical Architectures
“With the logical centralization of data access, logical
centralization of data governance, audit trails, security
and monitoring is possible.”
– Gartner: Assessing the Relevance of Data Virtualization in Modern Data
Architectures, June 2021
Logical Architectures
• Logical: Consumers access data through semantic
models, decoupled from data location and physical
schemas
Benefits (location, format, latency agnostic)
• Business friendly: Expressed in language of the
business
• Simple: Unified semantics, security, and governance
• Futureproof: Allows for technology evolution and
infrastructure changes
Start connecting.

18. DENODO LUNCH AND LEARN ASEAN
Logical Data Fabric
Faster Time-to-Value

19. 19
Denodo Platform: ONE Logical Platform for All Your Data
Logically Integrate, Manage, and Deliver Distributed Data
ANY DATA
SOURCE
ANY DATA
CONSUMER
ANY PLATFORM ENVIRONMENT
On-Premises | Cloud | Multi-Cloud | Containerized
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

20. 20
Logical for Data, Modern for Business

21. 21
DATA FLOW
• Static data residing on-premise in Microsoft data sources
(MS SQL databases, Excel spreadsheets etc.) extracted,
transformed, and moved to Azure Cloud data repositories
(Azure Synapse Analytics, Azure Cosmos DB, etc.) by Azure
Data Factory.
• Real-time data (e.g. web-logs) loaded to Azure Databricks
with Azure HDInsights.
• Third-party on-premise (SAP, Oracle) and cloud data
(Salesforce etc.) connected to Denodo Data Virtualization
platform.
• Azure-based data sources (Azure Synapse Analytics, Azure
Data Lake Storage, Azure Databricks) connected to
Denodo Data Virtualization platform providing unified
hybrid abstraction layer.
• Data from Kafka topics can be virtualized in Denodo
platform for real-time alerting and dashboarding.
• All connected data sources are combined, secured, and
exposed as Data Services over SQL (6) and API (7)
interfaces.
• Exposed virtual datasets consumed by Power BI,
analytical applications, Data Science tools, Enterprise
Marketplace portals, any real-time and mobile
applications.
2
1
3
4
5
6
7
Denodo Data Fabric Architecture in Azure

22. 22
DATA FLOW
• Data from cloud and on-prem sources is loaded into
AWS relational and Hadoop-based stores for
analytical and operational processing.
• On premise data from applications, databases, files,
and other sources are virtualized by the Denodo
engine providing unified and secure access point.
• Structured, semi-structured, and unstructured data
residing in AWS stores is combined with the data
coming from the cloud applications and on-prem
data sources delivering the real-time gateway for
the end-user consumption. Required virtual data
marts are built inside Denodo Platform for AWS.
• Data is consumed by Amazon QuickSight or any
other BI or analytical tools through SQL-based
interfaces or by applications and other tools through
REST and OData APIs.
2
1
3
4
Denodo Data Fabric Architecture in AWS

23. DENODO LUNCH AND LEARN ASEAN
Customer Use Case
LeasePlan

24. 24
World leader in fleet management. Owns a fleet of 1.9 Million vehicles with 175,000 customers in over 32 countries
Vehicle Lifecycle | End-to-End Services Focus
LeasePlan

25. 25
`
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
à
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
Global Data Hub | Reference Architecture

26. 26
Benefits for LeasePlan
• Zero downtime for BI reporting
• Provide more innovative services
• Proactive damage detection
• Better passenger safety
• Reduce overall cost of any damage
• Accurately predict when a car may need to be serviced
• Schedule appointments for customers
• Predict maintenance

27. DENODO LUNCH AND LEARN ASEAN
Best Practices
To Get Started

28. 28
1. Determine your business requirements
§ Do the business users need data fast?
§ Do they need up-to-date data from the sources?
2. Decide which data fabric is the best option – balance benefits vs costs
§ Physical data fabric when heavy transformations need to be persisted
§ Logical data fabric when integrating all enterprise data – faster and fresh
§ Best option: Logical data fabric with cache
3. Start small with data fabric and grow big
§ Don’t boil the ocean!
Best Practices to Get Started with Data Fabric

29. DENODO LUNCH AND LEARN ASEAN
Q&A

30. Next Steps

31. 31
Get Started Today
Try Denodo for a Test Drive with a 30-
day free trial in the cloud marketplaces
CHOICE
Under your cloud account
SUPPORT
Community forum AND remote sales
engineer
OPPORTUNITY
30 minutes free consultation with
Denodo Cloud specialist
denodo.link/drive22

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