Migrating Dbt Project Across Platforms

If you're moving a dbt project between warehouses like Snowflake and Databricks, this walks you through the dialect conversion using dbt Fusion's real-time compiler as your error detector. The workflow is methodical: generate unit tests on the source platform to lock in expected outputs, switch your target, then iterate through compile errors until you hit zero warnings and zero errors. Fusion tells you exactly what's broken and where, so you're not guessing at SQL differences. The skill emphasizes using dbtf compile as your primary feedback loop since it's free and catches issues before you burn warehouse credits on runs. It's thorough about leaf node testing and includes a progress checklist, though you'll need Fusion installed to make any of this work.

skills.sh installs: 528
GitHub stars: 759
last updated: May 19, 2026

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Install

$ npx -y skills add dbt-labs/dbt-agent-skills --skill migrating-dbt-project-across-platforms --agent claude-code

Installs into .claude/skills of the current project.

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Migrating a dbt Project Across Data Platforms

This skill guides migration of a dbt project from one data platform (source) to another (target) — for example, Snowflake to Databricks, or Databricks to Snowflake.

The core approach: dbt Fusion compiles SQL in real-time and produces rich, detailed error logs that tell you exactly what's wrong and where. We trust Fusion entirely for dialect conversion — no need to pre-document every SQL pattern difference. The workflow is: read Fusion's errors, fix them, recompile, repeat until done. Combined with dbt unit tests (generated on the source platform before migration), we prove both compilation correctness and data correctness on the target platform.

Success criteria: Migration is complete when: