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Build something that is not there yet

Author an incremental dbt model with its contract and prove idempotency

  • When a fact table is too big to rebuild from scratch and has to be incremental from the first version.
  • When an incremental model is proposed and nobody has proven yet that rerunning it twice is safe.

Author an incremental dbt model with a dedup-safe unique key and its paired YAML contract, and prove in the sandbox that a rerun is idempotent and a late-arriving update changes exactly the expected row. Covers the model and its contract, not the source system that feeds it.

Area
Transformation
Runs on
  • Microsoft Fabric Lakehouse
  • Microsoft Fabric Warehouse
  • MotherDuck
  • DuckDB
Built with
  • dbt
Readiness
ProvenA dedicated end-to-end case exercises this exact Recipe 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 `incremental-dbt-model-with-contract` at https://getvibedata.ai/cookbook/incremental-dbt-model-with-contract Read the Recipe and execute it in the context of the current Intent.

Recipe id incremental-dbt-model-with-contract · 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.

  • a repeated run changes no rows
  • a late-arriving update changes exactly the expected row and nothing else
  • every column in the contract is typed and tested
  • a missing source column stops the build rather than being guessed
Recipe promptThe task specification the agent reads. Reference only — it is not what you copy.
Deliver: Author an incremental dbt model with its contract and prove idempotency.

Author an incremental dbt model with a dedup-safe unique key and its paired YAML contract, and prove in the sandbox that a rerun is idempotent and a late-arriving update changes exactly the expected row. Covers the model and its contract, not the source system that feeds it.

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:
- a repeated run changes no rows
- a late-arriving update changes exactly the expected row and nothing else
- every column in the contract is typed and tested
- a missing source column stops the build rather than being guessed

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.

Profile the inputs the grain, joins and measures actually depend on before proposing a model. Put the design up for review — grain first — then build in an isolated copy with tests and documentation landing beside the model rather than after it.

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.

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. State the outcome in one sentence, in the language the request arrived in.
  2. Let it profile the inputs the grain, the joins and the measures actually depend on.
  3. Review the design. Disagreeing about grain here costs a sentence; after the model exists it costs a rewrite.
  4. Let it build in an isolated copy, with the tests and the documentation landing beside the model rather than after it.
  5. Read the acceptance conditions against the run.

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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