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Build something that is not there yet

Publish a Direct Lake semantic model over governed gold tables

  • When the gold marts exist and the BI team is about to rebuild the star schema by hand in Power BI.
  • When a Direct Lake model needs relationships and measures generated from tables that already have declared keys.

Generate a Direct Lake semantic model over governed gold Delta tables, with relationships from the declared keys and measures from agreed metric definitions, committed as code. Covers the model definition and a check that no table falls back to DirectQuery, not report design on top of it.

Area
Semantic layer
Runs on
  • Microsoft Fabric Lakehouse
Built with
  • Power BI
Readiness
PlannedAt least one capability or object type this Recipe needs is not supported yet.

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.

Review planned Recipe

At least one capability or object type this Recipe needs is not supported yet.

Read the VibeData Recipe `fabric-direct-lake-semantic-model` at https://getvibedata.ai/cookbook/fabric-direct-lake-semantic-model At least one capability or object type this Recipe needs is not supported yet. Treat it as a reference, not as work to execute.

Recipe id fabric-direct-lake-semantic-model · 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 model frames successfully over the declared gold tables
  • no table in the model falls back to DirectQuery
  • relationships in the model match the declared keys, not an inferred join
  • the model definition is committed as code
Recipe promptThe task specification the agent reads. Reference only — it is not what you copy.
Deliver: Publish a Direct Lake semantic model over governed gold tables.

Generate a Direct Lake semantic model over governed gold Delta tables, with relationships from the declared keys and measures from agreed metric definitions, committed as code. Covers the model definition and a check that no table falls back to DirectQuery, not report design on top of 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:
- the model frames successfully over the declared gold tables
- no table in the model falls back to DirectQuery
- relationships in the model match the declared keys, not an inferred join
- the model definition is committed as code

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

  • metric definition over governed tables
  • Power BI in the project
  • isolated-copy execution and gate verification

Guardrails

  • Build in an isolated copy. Production is read, never written.
  • At least one capability this Recipe needs is not supported yet. Say so and stop rather than substituting a different approach.

What you need

  • Microsoft Fabric Lakehouse.
  • Governed tables to define metrics over, and agreement on what each metric means.
  • Power BI 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 semantic layer 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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