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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 modeling techniques

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Product type Paperback
Published in Sep 2025
Publisher Packt
ISBN-13 9781837028030
Length 432 pages
Edition 2nd Edition
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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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Toc

Table of Contents (28) Chapters Close

Preface 1. Core Concepts in Data Modeling and Snowflake Architecture
2. Unlocking the Power of Modeling FREE CHAPTER 3. An Introduction to the Four Modeling Types 4. Mastering Snowflake’s Architecture 5. Mastering Basic Snowflake Objects 6. From Logical Concepts to Snowflake Objects 7. Mastering Advanced Snowflake Objects 8. Seeing Snowflake’s Architecture Through Modeling Notation 9. Applied Modeling from Idea to Deployment
10. Putting Conceptual Modeling into Practice 11. Putting Logical Modeling into Practice 12. Database Normalization 13. Database Naming and Structure 14. Putting Physical Modeling into Practice 15. Solving Real-World Problems with Transformational Modeling
16. Putting Transformational Modeling into Practice 17. Modeling Slowly Changing Dimensions 18. Modeling Facts for Rapid Analysis 19. Modeling Semi-Structured Data 20. Modeling Hierarchies 21. Data Modeling for Enterprise Teams
22. Scaling Data Models through Modern Techniques 23. Unlocking Value for Enterprise Organizations 24. Unlock Your Exclusive Benefits 25. Other Books You May Enjoy
26. Index
Appendix

Summary

In this chapter, we demonstrated that Snowflake objects pack a lot of features, even behind familiar ones such as tables and views. A table in Snowflake can store more than just the data—depending on its settings, it can also hold months of historical and disaster recovery backups and offer offset change tracking for CDC. Views, likewise, exceed expectations by providing change tracking and automated re-materialization.

We saw how stages mark the entry point for data to make its way from external sources to Snowflake tables. Stages also provide helpful features, such as external table access for reading file contents without copying them to a table beforehand.

Finally, to coordinate incoming data, establish automated ELT pipelines, and streamline CDC, Snowflake pairs tasks with streams to give its users full serverless or managed control—tying stages, tables, views, and all the connective transformational logic together.

Having understood the strengths...

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