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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

History patterns

In the previous chapters, we have seen how to define proper HKEY and HDIFF fields, use the save_history macro to store the versions of the entities that we see over time, and use the current_from_history macro to read the current version for each instance of the entity out of the history table.

We have also seen how we can use the HDIFF field as a version identifier to be used in facts when we do not just care about the current version but we want to use dimensions with all the historical versions, also known as SCDT2 dimensions.

When talking about how to calculate the open positions in our portfolio from some periodic extract of the active portfolio, we introduced the problem of deletions from source data, and we created a solution to detect deletions to recognize closed positions.

One important caveat of the loading pattern of the save_history macro is that it can handle one version per key, per load. This is simple to achieve by using a QUALIFY clause in...

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