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

Modeling the data marts

This section will explore the Star and Snowflake schemas—popular options for architecting user-facing self-service schemas and data marts due to their efficiency and ease of understanding. Both approaches are designed to optimize the performance of data analysis by organizing data into a structure that makes it easy to query and analyze. But first, a quick overview of what a data mart is.

Data mart versus data warehouse

A data warehouse and a data mart are repositories for storing and managing data, but they differ in scope, purpose, and design. A data warehouse is a large, centralized repository of integrated data used to support decision-making and analysis across an entire organization. Data warehouses are optimized for complex queries and often use Kimball’s dimensional modeling technique or Inmon’s 3NF approach (described in his book Building the Data Warehouse). On the other hand, a data mart is a subset of a data warehouse designed...