Data Engineering Podcast Unlocking Your dbt Projects With Practical Advice For Practitioners
Learn practical advice for building and scaling dbt projects, including adopting and using dbt, complexities of data lakes, challenges with YAML in dbt projects, scaling dbt projects, and importance of planning and structure in dbt.
01:16:04
Switching Stacks Led To dbt Discovery
- Dustin moved from building warehouses in Microsoft/Synapse to an AWS stack and discovered dbt, Snowflake and Fivetran fit his SQL-first approach.
- He found dbt consolidated stored-procedure orchestration and transformation patterns into a single, powerful tool.
Podcast Sparked A Self-Taught dbt Journey
- Cameron first heard about dbt on a podcast in 2019 and began self-teaching because he wanted better organization than 150 SQL files on his laptop.
- He migrated from stored procedures to dbt for deployments, testing, and promoting analytic work across environments.
Plan Your Project Before Coding
- Before writing any dbt code, design a practical plan for project structure, modeling, and governance.
- Iterate as you learn, but begin with constraints to avoid chaotic, unmaintainable models.
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Introduction
00:00 • 3min
Transitioning from Theology to Working in Tech
02:33 • 3min
Adopting and Using DBT
05:58 • 11min
The Complexities of Data Lakes, Introducing Starburst, and DBT Adoption and Skills
17:24 • 5min
Unlocking Your dbt Projects With Practical Advice For Practitioners
22:53 • 18min
Challenges of Scaling DBT Projects and Managing Technical Debt
40:33 • 2min
The Importance of Planning and Structure in DBT
42:50 • 33min
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
- Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro.
- Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
- You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free!
- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
- 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
- Biobot Analytic
- Breezeway
- dbt
- Synapse Analytics
- Snowflake
- Fivetran
- Analytics Power Hour
- DDL == Data Definition Language
- DML == Data Manipulation Language
- dbt codegen
- Unlocking dbt book (affiliate link)
- dbt Mesh
- dbt Semantic Layer
- GitHub Actions
- Metaplane
- DataTune Conference
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Miro:  Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at [dataengineeringpodcast.com/miro](https://www.dataengineeringpodcast.com/miro).
- Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
- Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
- Materialize:  You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing. Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access) today and get 2 weeks free!