Watch full webinar here: https://bit.ly/3xepiQa
Denodo customer McCormick created a logical data fabric (LDF) with data virtualization, to create Enterprise Data Service (EDS) for self-service analytics, integration, web and mobile applications. Listen to this presentation to learn how McCormick uses LDF for better business decisions and strategic planning via democratized information assets and in the process minimize information consumption risks via centralized security model.
6. We are like you
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7. What is a Data Fabric?
I have current challenges that I am facing!
What do I do with a mesh?
What does all this have to do with Analytics?
What does all this mean for the cloud?
We are like you
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8. We share your experiences
✔ Questions
✔ Challenges
✔ Aspirations
✔ Opportunities
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9. “Plateau of Productivity”
In the Gartner Emerging Technologies Hype
cycle report released Aug. 23, 2021, “data
fabric” sits right at the “Peak of Inflated
Expectations” (along with Nonfungible
Tokens or NFTs, and decentralized identity).
Forrester called data fabric “a hot,
emerging market,” in its Enterprise Data
Fabric Forrester Wave report published in
Q2 of 2020.
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10. What about Now?
Greatidea, but how real is the idea of data fabric for the typical enterprise organization right now?
August 25, 2021 https://www.informationweek.com/big-data/what-cios-need-to-know-about-data-fabric
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11. The Big Ideas
• Data Products
• Domain Knowledge
• Multiple Delivery Types
• Meta Data
• Entry Point
• Optimization
• Flexibility
• Low Maintenance
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12. What if
• IT could manage a framework, but not “control” it
• Business Users could find things and use them
• Simplify and Share data easily
• What if things could “adjust” transparently
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13. A Journey
• Five Years
• Machine Learning
• Application
• Cloud Migration
• Analytics
Integration
Can we leverage what we do?
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14. Why I Like Data Virtualization
Flexible Optimization
Abstraction Blank Canvas
We made it a foundational element!
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16. Data Asset Defined
A component comprised of a security model, meta data and
enterprise data which represents an entity known to the business
enterprise and exists for the purpose of creating data products
Examples are product specifications, invoices, production orders, general ledger, customer hierarchy, etc.
--Terry Dorsey
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19. PHYSICAL
LOGICAL
ASSET
BUSINESS
REPORTING
MODELING
Enterprise Data Services
Consumer Data Services
Physical Layer – Represents the Physical
Data Model. Contains Authentication Details
and ability to access information in source
Logical Layer – Represents the Logical Data
Model. Assimilation of information for
specific Entities/Objects.
Asset Layer – Controls Data Restrictions and
allows Security to Audit access to Specific
Roles
Business Layer – Represents Business
Entities. These views reflect the Enterprise
Conceptual model of Assets and
Transactions.
Modeling – First Consumer Data Services
Layer. Architecting of views for specific
reporting purposes. Uses Business Layer
views.
Reporting – Views to support specific
reporting requirements
EA determines the approach to layered development. These
databases are listed as examples only.
EDS manages connectivity, security and enterprise naming..
Layered Development
Enterprise Data Catalog
20. PHYSICAL
LOGICAL
ASSET
BUSINESS
REPORTING
MODELING
Enterprise Data Services
Consumer Data Services
EDS manages connectivity, security
and enterprise naming..
Enterprise Data Catalog
PHYSICAL
LOGICAL
ASSET
APPLICATION
REST
REST ODBC
Architected Views for Reporting and
Self-Service Analytics
Application/Integration Instances Reporting Instances
BUSINESS
MODELING
21. ASSET
PHYSICAL LAYER
Responsibility
• Connectivity to Source Systems
• Time Zone Settings
• Global Standardization
LOGICAL
BUSINESS
Enterprise Data Services
Security
• Restricted Access to Internal Resources
• Promote views to Logical Layer for Development
• Granular access in Source
PHYSICAL
22. LOGICAL LAYER
Responsibility
• Assimilation (Minimal)
• Semantic Naming
PHYSICAL
ASSET
BUSINESS
Enterprise Data Services
Security
• Restricted Access to Developers Only
• Access Specific views promoted from Physical Layer
LOGICAL
23. ASSET
ASSET LAYER
Responsibility
• Semantic Naming
• Security
• Interface to Deployed Content or Enterprise Assets
PHYSICAL
LOGICAL
BUSINESS
Enterprise Data Services
Security
• Security Auditing
• Access to Developers and Security Auditors
24. ASSET LAYER
Enterprise Data Services
Customer
Secured Interface
BETA Corp
ALPHA Corp
β
α
δ
ALPHA Group
BETA Group
How it Works
• User in Active Directory Group
• Active Directory Group connected to Denodo Role
• Denodo Role Restricted to rows and/or columns PHYSICAL
LOGICAL
BUSINESS
ASSET
25. BUSINESS LAYER
Responsibility
• User Accessible Layer
• Available via Data Catalog
• Published Views
PHYSICAL
LOGICAL
ASSET
Enterprise Data Services
Security
• Inherits Restrictions from Asset Layer
• Uses "Static" Interfaces to prevent propagation
BUSINESS
26. BUSINESS LAYER
Enterprise Data Services
✔ Everyone
Access to Business Layer
Development Artifacts:
• “Published” Assets (Views)
• Inherited Security Restrictions
Customer
Security
Auditor
Customer
Secured Interface
ASSET
EDS Developer
BUSINESS
Business Users
27. Product Development Finance
Manufacturing
Supply Chain
Marketing
Consumer Data Services
Enterprise Data Services
•Democratization
•Security
•Semantics
•Assimilation
•Source Connectivity
Business
Application
Physical
Logical
Asset
Business
✔ Managed Connectivity to Sources
✔ Asset-Based Security
✔ Inherited Restrictions
✔ Few Deployed Views
✔ Many “Architected” Views
Self Service and Self Governance
Organic Growth
28. Types of Users
• Developers
• Services (Special Data Consumers)
• Data Consumers
• Analysts
29. Data Consumers
• Data Catalog
✔ Meta Data
✔ Data Lineage
✔ Searching
• Access
✔ Business
✔ Domain Specific
✔ Role Based
• Read
✔ View Access
✔ Row Restrictions
✔ Sensitive Fields Restrictions
✔ Other
31. What We Achieve
• Assets are shared across integrations, applications, analytics, etc.
• Low/No Customization in Systems for integrations
• Reuse and Extension of Enterprise Data Service
• Transparent to Existing Consumers
• Single point of maintenance for data
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32. ✔ Data Assets as Components
✔ Point of Origin
✔ Inherited Security
✔ Domain Assets
✔ Logical Models
Reuse
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38. • Platform for Observation
• Information to Build Knowledge
• Foresight for performance improvement
• Foundation for ML and AI influence
• Leveraging the Logical Data Fabric
Opportunities
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