Watch full webinar here: https://bit.ly/3y4yMPU It’s almost impossible to find any organization that does not have data and analytics as one of their top priorities to further their business objectives. At the same time the data and analytics landscape is evolving faster than ever, making the data management ecosystem more complex than ever before. As data gets increasingly distributed across systems and locations, every forward looking organization should adopt a logical architecture to be future ready. Watch On-Demand and Learn: - Key priorities of data and analytics leaders for business transformation - Why a monolithic and physical data architecture is not suitable for such transformation - How a logical data architecture can help organizations in their business transformation

1. DATA VIRTUALIZATION
Packed Lunch Webinar Series
Sessions Covering Key Data Integration
Challenges Solved with Data Virtualization

2. Adopting a Logical Data Architecture for
Today’s Data and Analytics
Saptarshi Sengupta
Sr. Director of Product Marketing, Denodo

3. Agenda
1. Key Priorities of Data and Analytics Leaders
2. Evolution of Data Infrastructure
3. Solution: Monolithic or Distributed
4. Logical Data Architecture for Business Transformation

4. Key Priorities of Data and
Analytics Leaders

5. 5
Key Priorities of Data and Analytics Leaders
1. Democratization of enterprise data trove for all types of users
• Users don’t want to waste time searching across different data sources
• IT doesn’t want their users having access to all their production systems
2. Creation of a self-service culture for data consumers
• Users don’t want to have to learn to code (SQL, Python, Java, etc)
• They want to use the tools they’re most comfortable with
3. Secure & governed data access across multiple systems
• Centralized security and governance in any hybrid/multi-cloud environment
• Minimize the risk of a data breach & comply easily with data privacy rules

6. Evolution of Data
Infrastructure

7. 7
Conflict Between Centralizing and Decentralizing Data
Stop collecting, Start connecting

8. 8
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
Distributed Data Landscape 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

9. Solution:
Monolithic or Distributed

10. 10
Current State Architecture of Real Estate Customer

11. 11
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

12. 12
Logical Architecture – Path to the Future
Stop collecting, Start connecting

13. 13
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
…for Data Integration and Management
Start connecting.

14. Logical Data Architecture
for Business
Transformation

15. 15
BI Tools Data Science Tools
Logical Data Architecture with Denodo Platform
DATA CATALOG
Discover - Explore - Document
DATA AS A SERVICE
RESTful / OData
GraphQL / GeoJSON
SQL
CONSUMERS
DATA VIRTUALIZATION
INTEGRATE
to disparate data
in any location, format
or latency
MANAGE
related data into views
with universal semantic
model
DELIVER
using BI & data science
tools, data catalog,
and APIs
Self-Service
Hybrid/
Multi-Cloud
Query
Optimization
AI//ML
Recommendations
Security
LOGICAL
DATA
FABRIC
SOURCES
150+
data
adapters
Apps Streaming
Data
Governance
SaaS
Files
OLAP
Hadoop
& NoSQL
Cloud
Stores
Traditional
DB & DW

16. 16
Data Virtualization: Foundation for Logical and Distributed Architectures
“Data Virtualization capabilities offer an access and delivery layer that can serve as the foundation for the logical data fabric, which offers
significant automation functions in the data management space. These include automation of data recommendations, data quality, data
governance and policy, on top of the core integration functions of data virtualization.”
– Gartner: Assessing the Relevance of Data Virtualization in Modern Data Architectures, June 2021
6 Key Capabilities in Logical and Distributed Data
Architectures
Differentiated Use Cases Personas
Hybrid/Multi-
Cloud Data
Integration
Self-Service BI
Lakehouse/
Analytics Query
Accelerator
3600 View
of Entities
Security,
Governance and
Compliance
Data Science
Enterprise
Data Services
Data Fabric/
Data Mesh
Data/BI
Analysts
Data
Scientists
Architects
Data Engineers/
Developers
Data Ops
Admin
Real-Time
Data Integration
Logical Data
Abstraction
Smart Query
Acceleration
Advanced
Semantics
Automation &
Recommendation
Unified Security
& Governance
Data Catalog
AI/ML

17. Fireside Chat: Adopting a Logical Data
Architecture for Today’s Data and Analytics
Saptarshi Sengupta
Sr. Director of Product
Marketing, Denodo
Arif Rajwani
Partner, BDO Consulting

18. +
Becoming a part of BDO’s Technology Consulting team
Together, we meet the evolving needs of
clients and create opportunity
THE
TEAM
Vincent L’Archeveque
Partner
Technology Consulting
vlarcheveque@bdo.ca
Mano Vajpey
Partner
Technology Consulting
mvajpey@bdo.ca
Arif Rajwani
Partner
Technology Consulting
arajwani@bdo.ca
Clients across
North America
Leader in Data
Management
Serving 16+
Industries
Unified Data
Platforms
BDO WELCOMES
SIMPLICITY BI
Data Expertise Modern Workplace Business Operations Cloud Optimization

19. Q&A

20. 20
Get Started Today
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21. Thanks!
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