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 Semi-Structured Data

So far, this book has focused on modeling structured data, the kind used in relational databases since the early 70s. However, with the rise of the internet, a different style of data became prevalent: semi-structured. Semi-structured data, such as website traffic and social media feeds, contain some organizational structure but do not conform to the formal structure of a relational database.

New file formats also emerged to support this new type of data, starting with the advent of Extensible Markup Language (XML) in the early 2000s, followed by JavaScript Object Notation (JSON), and, with the rise of distributed computing, formats such as Avro, ORC, and Parquet. These formats offered a lightweight and flexible way to structure data, making them ideal for web-based and mobile app data.

The popularity of semi-structured data can be attributed to its flexibility, adaptability, and ability to handle data sources that do not fit neatly into traditional...