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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

Concepts

Author (1):

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

Why subscribe?

20. Other Books You May Enjoy

Working with Git in dbt Cloud

When working with dbt Cloud, you must have a Git repository to store your code.

You can use your own Git providers, such as GitHub, GitLab, Azure DevOps, Bitbucket, or others that implement the standard HTTPS or SSH Git communication interfaces. If you do not have a Git provider, dbt Cloud can use and internally manage the open source Git implementation.

It is important to note that branch and merge are Git concepts and are, therefore, always available, while PRs are totally external to Git and are a collaborative tool developed and powered by the individual Git provider.

If you use one of the Git providers that dbt Cloud has developed an integration for, (currently GitHub, GitLab, and Azure DevOps), you will already have PRs configured and a Continuous Integration (CI) process available when you open or propose a change to a PR.

In all other cases, to use PRs you just need to configure the correct URL so that dbt Cloud allows you to open a...

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Authors (1)

Roberto 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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