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Data Modeling with Snowflake

You're reading from  Data Modeling with Snowflake

Product type Book
Published in May 2023
Publisher Packt
ISBN-13 9781837634453
Pages 324 pages
Edition 1st Edition
Languages
Author (1):
Serge Gershkovich Serge Gershkovich
Profile icon Serge Gershkovich

Table of Contents (24) Chapters

Preface 1. Part 1: Core Concepts in Data Modeling and Snowflake Architecture
2. Chapter 1: Unlocking the Power of Modeling 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

Design and process

Over the years, many books have been written about modeling and designing database systems. However, many of these books fail to make the connection between technicality and practicality—which are inextricably linked. Often, modeling books prioritize completeness—such as describing four different notations to express a relationship—and ignore usability (e.g., which notation would be most accessible to business users?).

The most significant complexity of a database system is not technical. It is the business model itself—the interactions within a business and the rules that govern them. If the business model is not aligned with the data model, the database will be repeatedly forced to adjust (while losing organizational trust and resources in the process.)

What is needed is a business-readable language that developers use to build and document the database landscape.

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