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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

Product type Paperback

Published in Jun 2023

Publisher Packt

ISBN-13 9781803246284

Length 578 pages

Edition 1st Edition

Languages

SQL

Tools

dbt

Concepts

Data Engineering

Author (1):

Roberto Zagni

View More author details

Table of Contents (21) Chapters

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

Why subscribe?

20. Other Books You May Enjoy

Summary

Congratulations!

In this chapter, you discovered and applied more advanced patterns to improve the quality of your projects and experimented with how to evolve your code through refactoring.

You also learned how to save the history of changes of your entities, how to detect deletion from a source, how to better handle the orphans of your facts using self-completing dimensions, and how to use window functions to leverage the data stored in the HIST tables to analyze data evolution over time.

In the next chapter, Patterns for Common Use Cases, you will find a small library of common use patterns for ingesting data in Snowflake to extend the range of history tables, as well as a few more specialized patterns.

The rest of the chapter is locked

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Authors (1)

Zagni

Zagni

Roberto Zagni is a senior leader with extensive hands-on experience in data architecture, software development and agile methodologies. Roberto is an Electronic Engineer by training with a special interest in bringing software engineering best practices to cloud data platforms and growing great teams that enjoy what they do. He has been helping companies to better use their data, and now to transition to cloud based Data Automation with an agile mindset and proper SW engineering tools and processes, aka DataOps. Roberto also coaches data teams hands-on about practical data architecture and the use of patterns, testing, version control and agile collaboration. Since 2019 his go to tools are dbt, dbt Cloud and Snowflake or BigQuery.

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