Build something that is not there yet
Author a Fabric Data Pipeline that sequences ingestion, dbt, and downstream refresh as committed code
- When the pipeline was built by clicking in the workspace and lives nowhere a diff can show what changed.
- When an ingestion step, a dbt build, and a downstream refresh need to run in a fixed order and nobody has written the sequence down.
Author a Fabric Data Pipeline that sequences ingestion, the dbt build, and a downstream refresh step, committed as code with its schedule and dependency graph. Covers the pipeline definition and its validation, not the steps' own internal logic.
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 `fabric-data-pipeline-as-code` at https://getvibedata.ai/cookbook/fabric-data-pipeline-as-code Read the Recipe and execute it in the context of the current Intent.
Recipe id fabric-data-pipeline-as-code · 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 dependency graph passes static validation before any run
- an on-demand run completes the full sequence
- the schedule and dependency shape are documented alongside the pipeline
- every artifact the pipeline invokes exists in the repository before the pipeline references it
Recipe promptThe task specification the agent reads. Reference only — it is not what you copy.
Deliver: Author a Fabric Data Pipeline that sequences ingestion, dbt, and downstream refresh as committed code. Author a Fabric Data Pipeline that sequences ingestion, the dbt build, and a downstream refresh step, committed as code with its schedule and dependency graph. Covers the pipeline definition and its validation, not the steps' own internal 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 dependency graph passes static validation before any run - an on-demand run completes the full sequence - the schedule and dependency shape are documented alongside the pipeline - every artifact the pipeline invokes exists in the repository before the pipeline references it 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
- job scheduling and run evidence
- Fabric Data Pipelines 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, or Microsoft Fabric Warehouse.
- The jobs you want sequenced, and somewhere they are allowed to run.
- Fabric Data Pipelines in the project, or the intent to add it.
How it goes
- State the outcome in one sentence, in the language the request arrived in.
- Let it profile the inputs the grain, the joins and the measures actually depend on.
- Review the design. Disagreeing about grain here costs a sentence; after the model exists it costs a rewrite.
- Let it build in an isolated copy, with the tests and the documentation landing beside the model rather than after it.
- Read the acceptance conditions against the run.
What you end up with
The deliverable, in your own repository, as orchestration 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.