Migrating Dbt Project Across Platforms

---
name: migrating-dbt-project-across-platforms
description: Use when migrating a dbt project from one data platform or data warehouse to another (e.g., Snowflake to Databricks, Databricks to Snowflake) using dbt Fusion's real-time compilation to identify and fix SQL dialect differences.
user-invocable: false
metadata:
  author: dbt-labs
---

# 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](#additional-resources) — Reference docs for installation, unit tests, profile targets
- [Migration Workflow](#migration-workflow) — 7-step migration process with progress checklist