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migrating-dbt-project-across-platforms

Migrate dbt projects across data platforms using dbt Fusion compilation.

1|Updated Apr 6, 2026
Search Tags:#migration#dbt#unit-tests#data-warehouse#dbt-fusion#sql-dialects
One-click install
npx skills add https://github.com/pkoka888/server-infra-templates --skill migrating-dbt-project-across-platforms-pkoka888
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: migrating-dbt-project-across-platforms
Source: https://github.com/pkoka888/server-infra-templates/tree/main/.kilo/skills/marketplace/dbt-migration/skills/migrating-dbt-project-across-platforms
Command: npx skills add https://github.com/pkoka888/server-infra-templates --skill migrating-dbt-project-across-platforms-pkoka888

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Guides engineers through migrating a dbt project between data platforms by identifying and resolving SQL dialect, config, and package incompatibilities while preserving data correctness.

Core Features & Use Cases

  • Real-time dialect detection: Uses dbt Fusion compilation output to surface exact file/line errors for platform-specific SQL and config issues.
  • Safe migration workflow: Stepwise process to generate unit tests on the source, switch targets, iterate with dbtf compile until zero errors/warnings, then validate with dbt test or dbtf build.
  • Practical guardrails: Clear "don't do" rules, cost-conscious validation (compile-first), and templates for documenting migration changes and tracking progress.
  • Use Case: Migrate a production project from Snowflake to Databricks by updating profiles.yml, converting platform-specific configs, fixing Fusion-reported errors, and proving parity with unit tests.

Quick Start

Switch the project's active target to the destination in profiles.yml, run dbtf compile on the destination, fix Fusion's reported errors, and repeat until compilation shows zero errors and zero warnings.

Frequently Asked Questions about migrating-dbt-project-across-platforms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I migrate a dbt project from Snowflake to Databricks?▼

Migrate a dbt project across data warehouses by switching the target in profiles.yml, running dbtf compile on the destination, and fixing reported SQL dialect errors until compilation shows zero errors and warnings.

What is the best way to resolve SQL dialect incompatibilities when switching dbt data warehouses?▼

Resolve SQL dialect incompatibilities by using dbt Fusion compilation output to surface exact file and line errors for platform-specific SQL, then iterating with dbtf compile until the destination target compiles cleanly.

Do I need dbt Fusion to switch my dbt project between data platforms?▼

Yes, dbt Fusion is required for real-time compilation during dbt project migration. It acts as the primary iteration gate to detect dialect issues locally without incurring warehouse compute costs.

How do I validate data correctness after migrating dbt models to a new warehouse?▼

Validate data correctness after migration by generating unit tests on the source project, switching targets, and then running dbt test or dbtf build on the destination warehouse to prove parity.

Why should I compile dbt models before running tests against the destination data warehouse?▼

Compiling dbt models first is a cost-conscious validation step that catches platform-specific config and SQL issues without warehouse compute costs, ensuring a safe migration workflow before running tests.

What are the limitations of using dbt Fusion for dbt project migration?▼

A key limitation is that dbt Fusion compilation primarily surfaces SQL dialect and config incompatibilities; it does not replace full data validation, which still requires generating unit tests and running dbt test on the destination.

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