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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

Understanding the modern data stack

When we talk about data engineering, we encompass all the skillsets, tooling, and practices that cover the data life cycle from end to end, as presented in the previous section, from data extraction to user data consumption and eventually including the writing back of data.

This is a huge set of competencies and tools, ranging from security to scripting and programming, from infrastructure operation to data visualization.

Beyond very simple cases, it is quite uncommon that a single person can cover all that with a thorough understanding and good skills in all the areas involved, let alone have the time to develop and manage it.

The traditional data stack

The traditional data stack used to be built by data engineers developing ad hoc ETL processes to extract data from the source systems and transform it locally before loading it in a refined form into a traditional database used to power reporting. This is called an ETL pipeline.

The...

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