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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

Considerations before starting the implementation

When transitioning from a conceptual or logical design, where entities, attributes, relationships, and additional context have already been defined, there appears to be little to do at first glance when moving to a physical model. However, the specifics of Snowflake’s unique cloud architecture (discussed in Chapters 3 and 4), from its variable-spend pricing to time-travel data retention, leave several factors to consider before embarking on physical design. We’ll cover these factors in the following sections.

Performance

Query performance in Snowflake is heavily dependent on the clustering depth of the micro-partitions, which, in turn, are influenced by the natural sort order of the data inserted. Apart from Hybrid Unistore tables, which allow users to enable indexes, there are few performance tuning options left to the user besides sorting data before inserting and clustering. If the data volume in a given table...

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