sfc-gh-dflippo/snowflake-dbt-demo
Overview
This skill provides a complete, repeatable workflow for migrating tables, views, and stored procedures into production-quality dbt projects on Snowflake. It orchestrates discovery, planning, placeholder creation, view/procedure conversion, testing, and deployment while delegating platform-specific syntax translation to source-specific conversion helpers. The goal is predictable migrations with clear validation gates and a safe cutover path.
How this skill works
The workflow runs seven sequential phases: Discovery, Planning, Placeholders, Views, Table Logic, Testing, and Deployment. It first inventories and assesses source objects, creates contract-first placeholder models, converts views and procedural ETL into declarative dbt models, then validates results against the source using checksums, counts, and business-rule tests before cutover. Platform-specific SQL translation (e.g., Oracle→Snowflake) is handled by dedicated conversion utilities before dbt conversion.
When to use it
- Migrating an on-prem or cloud RDBMS (SQL Server, Oracle, Teradata, etc.) into dbt on Snowflake
- Replatforming legacy stored procedures and ETL jobs into declarative dbt models
- Standardizing naming, layering, and materializations across a newly migrated warehouse
- Validating parity between source systems and migrated dbt outputs before cutover
- Creating a repeatable process for large-scale, multi-object migrations
Best practices
- Start with a full inventory and dependency graph to define safe migration order
- Create placeholder models with explicit datatypes and where false before adding logic
- Convert simple views first to build confidence, then tackle complex views and procedures
- Map procedural ETL patterns to dbt materializations (incremental, table, snapshots)
- Automate validation: row counts, checksums, aggregates, and business-rule tests
- Define a clear cutover and rollback plan with post-deployment monitoring
Example use cases
- Use SnowConvert AI to translate Oracle procedures to Snowflake, then convert to dbt models
- Break a monolithic SCD Type 2 procedure into snapshot + incremental dbt models with tests
- Create placeholders for 1,000+ target tables to enable parallel development and downstream testing
- Run checksum and aggregate validations to prove parity before switching BI to Snowflake
- Implement scheduled production runs with Snowflake tasks or dbt Cloud after cutover
FAQ
Yes. A two-step approach is recommended: convert source platform SQL to Snowflake syntax, then migrate Snowflake objects into dbt models.
How do I handle complex stored procedures?
Analyze ETL patterns, split procedures into intermediate models, replace procedural constructs with CTEs, window functions, snapshots, or incremental strategies, and document conversion decisions.
18 skills
This skill guides you through migrating database objects to dbt on Snowflake, coordinating discovery, planning, placeholders, conversion, testing, and
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