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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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Toc

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

Summary

Good job!

In this chapter you have built a working data platform, with a sound architecture and clear organization, using only the basic features of dbt.

You now understand all the layers of our architecture, and you can build the appropriate models for each layer and save the changes to your data using dbt snapshots.

In the next chapter, we will learn all about incorporating dimensional data in our architecture and reinforcing everything we learned in this chapter.

Important note

Note that, in this chapter, we have been focused on giving you the basics of proper architecture and model organization in each architectural layer. To limit the page count and retain focus, we have deliberately disregarded some important activities, such as adding tests to verify the input source data and the data that we publish in the data mart. Do not assume they are not important.

We have also used some basic functionality, as we have not yet introduced the features to implement...

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