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How to Deploy a Data Diff Workflow
In terms of the DBT projects that you've seen, the way the data diff is being combined with the built-in capabilities of DBT. I'm wondering if you can talk through the types of failure modes and error conditions that you're able to catch with that combination. And maybe you need to bring in a more heavyweight testing and validation workflow.
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.
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Special Guest: Gleb Mezhanskiy.
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