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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
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Sonia Mezzetta
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Toc

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

DataOps’ value

DataOps accelerates operations in building and delivering data solutions. It can be viewed as a layer sitting on a Data Fabric architecture that expedites data processing and delivery to achieve an organization’s digital transformation journey. As discussed in Chapter 2, Show Me the Business Value, there are four key ingredients to achieve profitable data monetization:

  • Trusted quality data
  • Meaningful insights
  • Action-oriented business plan
  • High execution speed

DataOps with Data Fabric focuses on the delivery of trusted, quality data and meaningful insights, that is, insights that can be acted upon to derive value and reliability. Both the first and second points enable the creation of a lucrative business plan that can be executed on. Time is money, and DataOps and Data Fabric specialize in achieving the preceding four goals quickly.

The following is a summary of the key objectives of a DataOps discipline:

  • Customer satisfaction...
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