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Data Modeling with Snowflake

You're reading from   Data Modeling with Snowflake A practical guide to accelerating Snowflake development using universal data modeling techniques

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781837634453
Length 324 pages
Edition 1st Edition
Languages
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Author (1):
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Serge Gershkovich Serge Gershkovich
Author Profile Icon Serge Gershkovich
Serge Gershkovich
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Table of Contents (24) Chapters Close

Preface 1. Part 1: Core Concepts in Data Modeling and Snowflake Architecture
2. Chapter 1: Unlocking the Power of Modeling FREE CHAPTER 3. Chapter 2: An Introduction to the Four Modeling Types 4. Chapter 3: Mastering Snowflake’s Architecture 5. Chapter 4: Mastering Snowflake Objects 6. Chapter 5: Speaking Modeling through Snowflake Objects 7. Chapter 6: Seeing Snowflake’s Architecture through Modeling Notation 8. Part 2: Applied Modeling from Idea to Deployment
9. Chapter 7: Putting Conceptual Modeling into Practice 10. Chapter 8: Putting Logical Modeling into Practice 11. Chapter 9: Database Normalization 12. Chapter 10: Database Naming and Structure 13. Chapter 11: Putting Physical Modeling into Practice 14. Part 3: Solving Real-World Problems with Transformational Modeling
15. Chapter 12: Putting Transformational Modeling into Practice 16. Chapter 13: Modeling Slowly Changing Dimensions 17. Chapter 14: Modeling Facts for Rapid Analysis 18. Chapter 15: Modeling Semi-Structured Data 19. Chapter 16: Modeling Hierarchies 20. Chapter 17: Scaling Data Models through Modern Techniques 21. Index 22. Other Books You May Enjoy Appendix

Discovering Data Mesh

Data Mesh (DM) is an approach to organizing and managing data in large, complex organizations, introduced in 2019 by Zhamak Dehghani, a thought leader in the field of data architecture.

The DM approach advocates for decentralized data ownership and governance, with data treated as a product owned and managed by the teams using it. This contrasts with the traditional centralized (or, as Zhamak calls it, monolithic) approach to data management, where a single team or department is responsible for all data-related activities.

In a DM architecture, data is organized into self-contained domains, each responsible for its own data curation and sharing. These domains are often organized around business capabilities or processes and are staffed by cross-functional teams that include technical and business experts.

DM consists of four principles that aim to enable effective communication and collaboration between domains: domain-driven design, self-service, and...

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