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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

Keeping consistency by reusing code – macros

In the previous chapters, we used dbt to chain simple SQL-based transformations to produce refined information for our reports. That works fine and offers a good way of working with SQL code, but it would not be a momentous improvement over the previous tools available for the job.

One main difference that sets dbt apart from other tools in transforming data lies in its ability to easily define macros, which are very similar to functions in programing languages and allow us to reuse logic and pieces of SQL code to produce the final SQL code to be executed in the database.

The other huge difference is the simplicity of incorporating testing while writing code and running the tests along with the code to keep data quality in check, as we will see later in the next chapter.

You write dbt macros in a scripting language called Jinja, which is based on Python and used in many other Python-based projects.

Using macros, you can...

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