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

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781837634453

Length 324 pages

Edition 1st Edition

Languages

SQL

Tools

Snowflake

Concepts

Data Engineering

Author (1):

Serge Gershkovich

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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 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

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Appendix

Saving cash by using cache

With on-premises databases, inefficient operations resulted in longer execution times. In Snowflake’s variable spend model, that extra time is coupled with monetary penalties. Besides writing efficient SQL, Snowflake users should also understand the various caches associated with the service and virtual compute layers to understand where they can take advantage of pre-calculated results. A firm grasp of Snowflake caching will also inform decisions when modeling and building data pipelines.

Let us start with the services layer and familiarize ourselves with the caches it manages and offers its users.

Services layer

The services layer handles two types of cache: metadata and query results cache.

Metadata cache

The services layer manages object metadata, such as structure, row counts, and distinct values by column. Reviewing this metadata through related SQL functions or the Snowflake UI will not require a running warehouse and does not...