Summary
Data engineering is all about building workflows, pipelines, systems, and interfaces to provide stable and reliable data. Your data can be stable and wrong, but then it isn't reliable. Confidence in your data is achieved through constant validation and testing. Datafold has invested a lot of time into integrating with the workflow of dbt projects to add early verification that the changes you are making are correct. In this episode Gleb Mezhanskiy shares some valuable advice and insights into how you can build reliable and well-tested data assets with dbt and data-diff.
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 Gleb Mezhanskiy about how to test your dbt projects with Datafold
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Datafold is and what's new since we last spoke? (July 2021 and July 2022 about data-diff)
What are the roadblocks to data testing/validation that you see teams run into most often?
How does the tooling used contribute to/help address those roadblocks?
What are some of the error conditions/failure modes that data-diff can help identify in a dbt project?
What are some examples of tests that need to be implemented by the engineer?
In your experience working with data teams, what typically constitutes the "staging area" for a dbt project? (e.g. separate warehouse, namespaced tables, snowflake data copies, lakefs, etc.)
Given a dbt project that is well tested and has data-diff as part of the validation suite, what are the challenges that teams face in managing the feedback cycle of running those tests?
In application development there is the idea of the "testing pyramid", consisting of unit tests, integration tests, system tests, etc. What are the parallels to that in data projects?
What are the limitations of the data ecosystem that make testing a bigger challenge than it might otherwise be?
Beyond test execution, what are the other aspects of data health that need to be included in the development and deployment workflow of dbt projects? (e.g. freshness, time to delivery, etc.)
What are the most interesting, innovative, or unexpected ways that you have seen Datafold and/or data-diff used for testing dbt projects?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on dbt testing internally or with your customers?
When is Datafold/data-diff the wrong choice for dbt projects?
What do you have planned for the future of Datafold?
Contact Info
LinkedIn (https://www.linkedin.com/in/glebmezh/)
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning.
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