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