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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

Transformational

It all begins with SELECT. Modeling through transformational logic is a powerful and highly maneuverable method for modeling data that comes with one serious drawback: it needs existing data to SELECT from. Transformational modeling is rarely done in transactional databases because, in such systems, data is created and modified through the company’s operational processes (e.g., purchases and profile updates)—with which expensive transformational processes should not compete for critical system resources. However, in data warehouses, where conformed datasets are extracted and duplicated with fresh timestamps for each load, transformational modeling becomes a necessity.

Because transformational modeling selects from existing structured data, the result set is already structured. Selecting the SUPERHERO_NAME and HAS_MASK columns and creating a table will preserve their structure (VARCHAR and BOOLEAN, respectively). However, as with all modeling, transformations...

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