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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

Data Fabric architecture layers

Data Fabric architecture follows the nine principles discussed in the previous section. These principles establish the bedrock for a Data Fabric architecture that addresses data silos, enables data democratization, and creates a connected and intelligent data ecosystem of trusted, secure, and reliable data that supports data producers and data consumers. In Chapter 1, Introducing Data Fabric, we discussed Data Fabric design as having three building blocks:

  • Data Governance
  • Data Integration
  • Self-Service

Let’s represent these building blocks as layers in a Data Fabric architecture with specific responsibilities and supporting components:

  • Data Governance enables active Metadata Management and automated life cycle governance. Its knowledge layer is represented by a Metadata Knowledge Graph.
  • Data Integration handles inbound and outbound data management, data processing, and data engineering. This layer relies on active...
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