Unlocking Your dbt Projects With Practical Advice For Practitioners

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November 19th, 2023

1 hr 16 mins 4 secs

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About this Episode

Summary

The dbt project has become overwhelmingly popular across analytics and data engineering teams. While it is easy to adopt, there are many potential pitfalls. Dustin Dorsey and Cameron Cyr co-authored a practical guide to building your dbt project. In this episode they share their hard-won wisdom about how to build and scale your dbt projects.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
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  • Your host is Tobias Macey and today I'm interviewing Dustin Dorsey and Cameron Cyr about how to design your dbt projects

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • What was your path to adoption of dbt?
    • What did you use prior to its existence?
    • When/why/how did you start using it?
  • What are some of the common challenges that teams experience when getting started with dbt?
    • How does prior experience in analytics and/or software engineering impact those outcomes?
  • You recently wrote a book to give a crash course in best practices for dbt. What motivated you to invest that time and effort?
    • What new lessons did you learn about dbt in the process of writing the book?
  • The introduction of dbt is largely responsible for catalyzing the growth of "analytics engineering". As practitioners in the space, what do you see as the net result of that trend?
    • What are the lessons that we all need to invest in independent of the tool?
  • For someone starting a new dbt project today, can you talk through the decisions that will be most critical for ensuring future success?
  • As dbt projects scale, what are the elements of technical debt that are most likely to slow down engineers?
    • What are the capabilities in the dbt framework that can be used to mitigate the effects of that debt?
    • What tools or processes outside of dbt can help alleviate the incidental complexity of a large dbt project?
  • What are the most interesting, innovative, or unexpected ways that you have seen dbt used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working with dbt? (as engineers and/or as autors)
  • What is on your personal wish-list for the future of dbt (or its competition?)?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

  • Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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