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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

Adding dimensional data

In general, dimensional data is used to provide descriptive information about a fact by using the code of the dimension entity that is stored in the facts to join on the dimension table to retrieve the descriptive information.

The position fact that we loaded in the previous section has four explicit foreign keys, which we have aptly named with the _CODE suffix: the account code, the security code, the exchange code, and the currency code.

These four codes are the references, or foreign keys, to the four dimensions that we can directly connect to this fact.

There is also one extra implicit dimension, the bank dimension, which is implied in the names of the models.

Creating clear data models for the refined and data mart layers

To be able to finalize the dimensions and the fact design, we need to have a clear picture of the data model that we want to use in our reports (the data mart layer), which is often a star schema or, rarely, a wide table...

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