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Data Engineering with dbt

You're reading from   Data Engineering with dbt A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803246284
Length 578 pages
Edition 1st Edition
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Author (1):
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Roberto Zagni Roberto Zagni
Author Profile Icon Roberto Zagni
Roberto Zagni
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Table of Contents (21) Chapters Close

Preface 1. Part 1: The Foundations of Data Engineering
2. Chapter 1: The Basics of SQL to Transform Data FREE CHAPTER 3. Chapter 2: Setting Up Your dbt Cloud Development Environment 4. Chapter 3: Data Modeling for Data Engineering 5. Chapter 4: Analytics Engineering as the New Core of Data Engineering 6. Chapter 5: Transforming Data with dbt 7. Part 2: Agile Data Engineering with dbt
8. Chapter 6: Writing Maintainable Code 9. Chapter 7: Working with Dimensional Data 10. Chapter 8: Delivering Consistency in Your Data 11. Chapter 9: Delivering Reliability in Your Data 12. Chapter 10: Agile Development 13. Chapter 11: Team Collaboration 14. Part 3: Hands-On Best Practices for Simple, Future-Proof Data Platforms
15. Chapter 12: Deployment, Execution, and Documentation Automation 16. Chapter 13: Moving Beyond the Basics 17. Chapter 14: Enhancing Software Quality 18. Chapter 15: Patterns for Frequent Use Cases 19. Index 20. Other Books You May Enjoy

Saving history for the dimensional data

In the previous chapter, we saved the history of our first table, which was about positions. That is a FACT table, as it records information about facts that have happened or the state of a system at some point in time.

Facts may be subject to change. If they are, we need a business key to recognize the different instances of the fact, as we did for the positions. Alternatively, facts may be immutable, and then we do not need a business key, as is the case with web views.

Now we are going to save the history of our first DIMENSION.

Dimensions store descriptive information that allows us to fully comprehend a fact, such as information about a customer or, in our case, about securities.

The entities described by dimensions are long lived, often referenced by multiple facts at different times, and can generally change during their lifetime. Because of this, correctly defining the identity of the entity, hopefully with a business key...

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