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pezakas's avatar
pezakas
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1 year ago
Solved

Error : InvalidRequest_DatamartNotFound

Hello i am getting the following error when i am trying to refresh a direct lake semantic model in Fabric uat environment : Data source error{"error":{"code":"DM_InvalidRequest_DatamartNotFound","pb...
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  • pezakas's avatar
    pezakas
    1 year ago

    Yes i confirm that the mistake was that the semantic model on UAT was connected to the Lakehouse id instead of the sql analytics endpoint. Once i changed the id in the deployment pipeline and the connection everything was aligned. 
    Thank you.

v-dineshya's avatar
v-dineshya
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1 year ago

Hi pezakas ,

Thank you for reaching out to the Microsoft Community Forum.

 

You are facing the "InvalidRequest_DatamartNotFound" error when refreshing a Direct Lake semantic model in the Fabric UAT environment and also when trying to create visualizations in Power BI Desktop. Please check below reasons.

 

1. The object ID used in the semantic model doesn’t match any valid datamart or lakehouse in the target environment.

 

2. The deployment pipeline didn’t correctly update the connection references during promotion from Dev to UAT.

 

3. The semantic model is still pointing to a Dev artifact due to incomplete rule application.

 

Please check below things to fix the issue.

 

1. Validate Object ID Mapping, Open the semantic model in Power BI Desktop. Check the data source settings and confirm that the lakehouse connection is pointing to the correct UAT object ID. If needed, rebind the model manually to the UAT lakehouse.

 

2. Rebuild or Rebind the Semantic Model, If the object ID mismatch persists, consider rebuilding the semantic model in UAT using the correct lakehouse reference. Use Tabular Editor or Power BI REST API to script the rebind process if manual steps are too difficult.

 

3. Clear Direct Lake Cache, Use a Spark notebook connected to the UAT lakehouse to run a cache-clearing command. This forces Fabric to re-read the schema and may resolve metadata issues.

 

4. Check for Schema Changes, check that the lakehouse schema like tables, columns, relationships in UAT matches what the semantic model expects. If schema changes occurred post-deployment, refresh the model in Power BI Desktop to re-sync metadata.

 

I hope this information helps. Please do let us know if you have any further queries.

 

Regards,

Dinesh