Start fresh, don't lift and shift: a dbt migration guide

Daniel Poppy's guide emphasizes the pitfalls of a "lift-and-shift" approach when migrating to the Data Build Tool (dbt), where teams replicate their existing data workflows without re-evaluating or optimizing them, leading to continued inefficiencies and technical debt. He outlines common symptoms of poorly executed migrations, such as direct mapping of stored procedures to dbt models, lack of staging models, and inadequate testing and documentation, which result in fragile architectures that fail to take full advantage of dbt's capabilities. The guide advocates for a thoughtful migration strategy that involves triaging existing models to determine which should be eliminated, rewritten, or translated with care, and building new components based on business requirements. It stresses the importance of quality over mere completion, especially as AI systems increasingly depend on robust data infrastructure, and provides a checklist to ensure healthy migration practices that prioritize model clarity, testing, and documentation.

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