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Re-engineer something you own

Convert a scalar SQL function into a dbt macro and prove equivalence

  • When a scalar SQL function is called from a dozen models and lives outside version control.
  • When a database-level function needs to work the same way once dbt owns the logic.

Convert a scalar SQL function into a dbt macro and prove it returns the same result as the original function across its call sites. Covers the macro conversion and its equivalence proof, not redesigning the function's logic.

Area
Transformation
Runs on
  • Microsoft Fabric Lakehouse
  • Microsoft Fabric Warehouse
  • MotherDuck
  • DuckDB
Built with
  • dbt
Readiness
SupportedEverything this Recipe composes runs today, without a case that proves this exact shape.

Sample This Recipe has not been materialized in the Cookbook repository yet. Its trigger, description, prompt, agent guidance and acceptance conditions, and the explanation below, are prototype drafts. Its name, job, area and readiness come from the reconciled Cookbook seed snapshot. Readiness is a separate question from this one: it says whether the capability exists, not whether the writing has been reviewed.

Use this Recipe

Use the VibeData Recipe `scalar-sql-function-to-dbt-macro` at https://getvibedata.ai/cookbook/scalar-sql-function-to-dbt-macro Read the Recipe and execute it in the context of the current Intent.

Recipe id scalar-sql-function-to-dbt-macro · Not yet materialized in the Cookbook repository, so the pointer addresses this page.

Verified by

What has to be observably true before this Recipe is finished.

  • the macro returns the same output as the original function for every tested input
  • every model that called the function now calls the macro instead
  • the original function is not dropped in the same change that introduces the macro
  • the macro is documented with its arguments and return type
Recipe promptThe task specification the agent reads. Reference only — it is not what you copy.
Deliver: Convert a scalar SQL function into a dbt macro and prove equivalence.

Convert a scalar SQL function into a dbt macro and prove it returns the same result as the original function across its call sites. Covers the macro conversion and its equivalence proof, not redesigning the function's logic.

Execute inside the current Intent. Its Domain, repository, platform, environment and attached sources are the context for this work — read them rather than asking for them.

The work is done when:
- the macro returns the same output as the original function for every tested input
- every model that called the function now calls the macro instead
- the original function is not dropped in the same change that introduces the macro
- the macro is documented with its arguments and return type

Report the evidence for each condition above with the result. A condition you cannot meet is something to say, not something to work around.
Agent guidanceHow the agent approaches the work, and what it will not do.

Write down the behaviour that has to stay constant before you touch the current form, and get that agreed. Rebuild in an isolated copy, then reconcile old against new at the grain the consumers read. The reconciliation is the deliverable; the diff is not.

Composes

  • dbt model authoring and layering
  • dbt in the project
  • isolated-copy execution and gate verification

Asks first

Semantic decisions the Intent cannot supply. Never context Studio already holds.

  • which Fabric target this work lands on, when the Domain carries both a Lakehouse and a Warehouse

Guardrails

  • Build in an isolated copy. Production is read, never written.
  • Do not edit the object being held constant. Parity that required a change to the original is not parity.

What you need

  • Microsoft Fabric Lakehouse, Microsoft Fabric Warehouse, MotherDuck, or DuckDB.
  • A dbt project you can build, and read access to the models it starts from.
  • dbt in the project, or the intent to add it.

How it goes

  1. Name the thing you own, and what about its behaviour has to stay the same.
  2. Let it read the current form and write down the behaviour it is holding constant.
  3. Review the design before any SQL is written. Grain first.
  4. Let it rebuild in an isolated copy of your estate. Production is untouched throughout.
  5. Reconcile the new form against the old one, row by row.
  6. Read the acceptance conditions. They are the contract; the diff is not.

What you end up with

The deliverable, in your own repository, as transformation work a reviewer who knows the project reads as native to it. Alongside it, the evidence for every one of the acceptance conditions above — which is the part that is still there in three weeks when somebody asks.

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