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

Summary

In a DWH, fact tables present an additional challenge on top of merely capturing the latest values – they must also be able to capture and reconcile historical changes in a way that allows users to flexibly and cost-effectively query them to resolve business questions, because when it comes to operational analytics, analyzing changes, variations, and what didn’t happen can be just as valuable as the current state of truth.

Various types of fact tables exist to help an organization meet these demanding analytical needs, such as transactional, snapshot, and accumulating snapshot fact tables, among others. These fact tables must differentiate between the various kinds of measures they store (e.g., additive, semi-additive, and non-additive) because each is treated differently when updating or recording changes.

To help data teams construct and maintain these tables in Snowflake, this chapter dissected some of the toughest challenges in maintaining fact tables...