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Principles of Data Fabric

You're reading from   Principles of Data Fabric Become a data-driven organization by implementing Data Fabric solutions efficiently

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781804615225
Length 188 pages
Edition 1st Edition
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Authors (2):
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DR. Tommy Dang DR. Tommy Dang
Author Profile Icon DR. Tommy Dang
DR. Tommy Dang
Sonia Mezzetta Sonia Mezzetta
Author Profile Icon Sonia Mezzetta
Sonia Mezzetta
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Table of Contents (16) Chapters Close

Preface 1. Part 1: The Building Blocks
2. Chapter 1: Introducing Data Fabric FREE CHAPTER 3. Chapter 2: Show Me the Business Value 4. Part 2: Complementary Data Management Approaches and Strategies
5. Chapter 3: Choosing between Data Fabric and Data Mesh 6. Chapter 4: Introducing DataOps 7. Chapter 5: Building a Data Strategy 8. Part 3: Designing and Realizing Data Fabric Architecture
9. Chapter 6: Designing a Data Fabric Architecture 10. Chapter 7: Designing Data Governance 11. Chapter 8: Designing Data Integration and Self-Service 12. Chapter 9: Realizing a Data Fabric Technical Architecture 13. Chapter 10: Industry Best Practices 14. Index 15. Other Books You May Enjoy

Summary

A Data Fabric design represents a data architecture that is part of enterprise architecture. It’s critical for a data architecture to align and support the business capabilities designed as part of the business architecture for an organization. To design Data Fabric, it must follow nine recognized industry principles, which are data are assets that can evolve into data products, data is shared, data is accessible, data product ownership, common vocabulary and data definitions, data security, interoperability, always be architecting, and design for automation. These principles provide the necessary guardrails in its design to ensure it provides business value while following successful practices.

In this chapter, we provided an introduction to a logical Data Fabric architecture consisting of three layers: Data Integration focused on inbound and outbound data management; Data Governance, ensuring the right level of data integrity, security, and trust are in place with...

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