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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

Scaling Data Models through Modern Techniques

After covering theory, architecture, terminology, methodology, and Snowflake-centered transformation strategies throughout the book, this chapter will build upon that foundational knowledge to address common data management challenges in large, complex environments. Specifically, this chapter will explore Data Vault 2.0 and Data Mesh methodologies—popular solutions that have emerged in response to some of the biggest challenges facing large organizations today. Despite their similar naming, Data Vault and Data Mesh attempt to tackle very different challenges, and are often used together.

Data Vault is a methodology that focuses on the efficient and flexible storage of data, with a primary focus on auditing and effortless scalability. It is made up of three pillars: modeling, methodology, and architecture. Its standardized, repeatable design patterns can be applied regardless of the complexity of the data or how many source systems...

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