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Data Engineering with dbt

You're reading from  Data Engineering with dbt

Product type Book
Published in Jun 2023
Publisher Packt
ISBN-13 9781803246284
Pages 578 pages
Edition 1st Edition
Languages
Author (1):
Roberto Zagni Roberto Zagni
Profile icon Roberto Zagni

Table of Contents (21) Chapters

Preface 1. Part 1: The Foundations of Data Engineering
2. Chapter 1: The Basics of SQL to Transform Data 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

Advanced automation – hooks and run-operations

With environments, jobs, and configuration, you can design your architecture and deploy and run your data platform using one or more dbt projects, but other activities are needed to keep a data platform up and running beyond data transformations.

In this chapter, we will look at two advanced functionalities, hook and run-operation commands, and the use of the latter to handle the database migrations that are not handled by dbt.

Both these functionalities allow you to execute the arbitrary SQL that you need to complement dbt transformations. It could be from creating user functions to managing grants or doing database-specific operations such as cloning or vacuuming or creating shares or running any other command besides creating tables/views and transforming data.

The main difference between the two functionalities lies in when the SQL must be activated: connected to some model life cycle or independent of it.

Hooks

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