The document outlines the concept of a logical data fabric, emphasizing its ability to efficiently consolidate diverse data sources for analytics and insights automation. It highlights the key components and functionalities, such as data integration, virtualization, governance, and performance optimization supported by major analysts like Gartner and Forrester. Denodo's logical data fabric aims to enhance data management by delivering trusted data rapidly to various consumers.

1. #DenodoDataFest
Logical Data Fabric: Architectural Components
Executive VP & CTO, Denodo
AL B ERTO PAN

2. Agenda
1. Why a Data Fabric ?
2. Components of Data Fabric Architectures
3. Denodo Logical Data Fabric

3. 3
The core of the matter is being able to consolidate many diverse data sources in an
efficient manner by allowing trusted data to be delivered from all relevant data
sources to all relevant data consumers through one common layer.
Source: Demystifying the Data Fabric, Gartner, September 2020
The Data fabric focuses on automating the process integration, transformation,
preparation, curation, security, governance, and orchestration to enable analytics
and insights quickly for business success.
Source: Enterprise Data Fabric Wave, Forrester, June 2020

4. 4
Data Fabric: Supported by the Major Analysts
Source: Forrester Enterprise Data Fabric Wave,
June 2020
Source: Demystifying the Data Fabric Gartner,,
September 2020

5. 5
Data Fabric: Data Integration and Delivery

6. 6
Data Virtualization: Logical Data Delivery for the Business
Development
Lifecycle
Monitoring & Audit
Governance
Security
Development Tools
/ SDK
Scheduler
Cache
Optimiser
JDBC/ODBC/ADO.Net REST / GraphQL / OData
U
LoB
View
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

7. Query Execution
Source
Abstraction
Virtual
Modelling
Business
Delivery
Query Optimizer
Security & Governance
Query Engine
Delegate processing to data sources
▪ Transparently switch workloads according to cost or
performance
Most advanced execution engine for distributed scenarios
▪ Unique techniques automatically rewrite user queries to
maximize pushdown
▪ Leverage MPP capabilities of data sources to deal with large
data volumes
Advancing Caching / Acceleration Mechanisms
▪ Selectively materialize subsets of the data for protecting data
sources and query acceleration

8. 8
Integrated ETL / ELT Support: Remote Tables
Create table in any location
Load with data from any other data source
Examples:
• Data Lake
management
• Replicate data in lake
when needed
• Data Science
• Move data to Spark
after initial analysis
• Cloud Migrations
• Replicate and
update data to
cloud system
SCH
Often triggered from
Scheduler

9. 9
Data Fabric: Graph-Based Semantics and Governance
Source: Forrester Enterprise Data Fabric Wave,
June 2020
Source: Demystifying the Data Fabric Gartner,,
September 2020

10. 10
Data Virtualization for Data Governance
Single Entry Point
for Enforcing
Security and
Governance
Policies
Single Source of
Truth / Canonical
Views
Who is Doing /
Accessing What,
When and How
Fewer copies of
personal data.
Lineage of copies
is available.
Data on-premises
and off, combined
through the same
governed virtual
layer

11. 11
Conformance with Semantic Models
11
Create or Import Semantic Models:
• RDF/OWL, Power Designer, ER Studio, Erwin,
IBM Data Architect…
“Contract” between modelers and developers
• Ensures data complies with standard data
models and governance rules
Allows parallel development of consuming
applications and virtual models

12. 12
Impact Analysis: Leverage Graph Relationships and Dependencies
Example: adding a new field to a data source
1
Views affected
by the change
2
Web Services
affected by the
change
3
Option to propagate
new field individually
per view
4
Preview of the
Tree view of the
affected views

13. 13
Data Fabric: Active / Augmented Data Catalog
Source: Forrester Enterprise Data Fabric Wave,
June 2020
Source: Demystifying the Data Fabric Gartner,,
September 2020

14. MY RECOMENDATIONS
Data Marketplace for the Business:
Discover and contextualize interesting
datasets
Search, query and Prepare Data.
Consume with any visualization /
reporting tool
Personalized recommendations
and shortcuts to most
used datasets. Think Netflix, but your data

15. 15
Denodo Data Catalog: Datasets Usage

16. 16
Data Fabric: ML for Task Automation
Source: Forrester Enterprise Data Fabric Wave,
June 2020
Source: Demystifying the Data Fabric Gartner,,
September 2020

17. 17
Automatically Recommend Caching / DM Strategies for Enhanced Performance
Denodo 8: ML for Smart Query Acceleration
SELECT PROD, SUM(PRICE) FROM…
SELECT CUST, COUNT(PRICE) FROM…
SELECT PROD, MIN (SALE_DATE) FROM…
SELECT PROD, SHOP, SUM (PRICE)
…
Previous Queries
SELECT PROD, CUST, SUM(PRICE),
COUNT(PRICE) …
SELECT PROD, SHOP, MIN(SALE_DATE),
SUM(PRICE)
…
Caching Expressions
(Intermediate Aggregates)
Cache Database
(or Data Source) Data Sources

18. 18
Key Takeaways
The Data Fabric plays a crucial role in the most pressing data management challenges today,
enabling agile delivery of trusted and governed data to any consumer
Major market analysts have identified the main components/pillars the Fabric needs to support
Denodo’s Logical Data Fabric allows to create a Data Fabric today accelerating the delivery of
trusted data from any location and to any consumer up to 80%

19. © Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written
authorization from Denodo Technologies.