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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

Setting Up Your dbt Cloud Development Environment

In this chapter, we will start to work hands-on with dbt.

We will start by setting up a development environment, first creating a free GitHub account with a repository for your dbt project, then creating a free dbt Cloud account to host your first project. We will connect the dbt project to the Snowflake account that we created in the first chapter.

Once we have dbt Cloud up and running, we will have a look at the differences between dbt Cloud and the open-source version, dbt Core. In the rest of the book, we will use dbt Cloud as it requires no installation and offers the same experience independent of your operating system, plus a host of extra services and functionalities.

In this chapter, you will start to learn about the data engineering workflow when working with dbt and why Version Control (VC) is important.

We will close the chapter by experimenting with some SQL we saw in the first chapter and then by looking at...

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