migrating-dbt-project-across-platforms

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:

1. dbtf compile finishes with 0 errors and 0 warnings on the target platform
2. All unit tests pass on the target platform (dbt test --select test_type:unit)
3. All models run successfully on the target platform (dbtf run)

Validation cost: Use dbtf compile as the primary iteration gate — it's free (no warehouse queries) and catches both errors and warnings from static analysis. Only dbtf run and dbt test incur warehouse cost; run those only after compile is clean.

Contents

• Additional Resources — Reference docs for installation, unit tests, profile targets
• Migration Workflow — 7-step migration process with progress checklist
• Don't Do These Things — Critical guardrails
• Known Limitations & Gotchas — Fusion-specific and cross-platform caveats