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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

Enhancing Software Quality

In this chapter, you will discover and apply more advanced patterns that provide high-quality results in real-life projects, and you will experiment with how to evolve your code with confidence through refactoring.

Through the selection of small use cases around our sample project, you will learn how to save the history of changes of your entities in a very efficient way, how to detect deleted rows from a source, and how to use window functions to leverage the data stored in HIST tables to analyze data evolution over time.

In the last section of this chapter, you will create and apply a macro to properly handle the orphans keys in your facts using self-completing dimensions to produce better quality facts and dimensions for your data marts.

In this chapter, you will learn about the following topics:

  • Refactoring and evolving models
  • Implementing real-world code and business rules
  • Publishing dependable datasets
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