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Data Modeling with Snowflake

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

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781837634453
Length 324 pages
Edition 1st Edition
Languages
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Author (1):
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Serge Gershkovich Serge Gershkovich
Author Profile Icon Serge Gershkovich
Serge Gershkovich
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Table of Contents (24) Chapters Close

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 22. Other Books You May Enjoy Appendix

Streams

Streams are logical objects that capture data changes in underlying sources, including the previously mentioned objects (physical tables, views, and external and directory tables). Whenever a DML operation occurs in the source object, a stream tracks the changes (inserts, deletions, and the before/after images of updates). Streams achieve this through an offset storage technique—logically taking an initial snapshot of data and then tracking changes through metadata columns. Although a stream can be queried like a table, it is not a separate object and does not contain table data.

When a stream is created, metadata columns are tacked onto the source object and begin tracking changes. The following table describes the metadata fields and their contents:

Figure 4.4 – Stream metadata columns

Figure 4.4 – Stream metadata columns

The following command creates a stream on a table:

CREATE STREAM <stream_name> ON TABLE <table_name>

For every subsequent DML...

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