Forum Discussion
Best Practices for Managing Incremental Refresh
Hi,
I'm currently working on a Power BI project where we have three separate workspaces: one for development, one for QA, and one for production. Each workspace is synced to a corresponding branch in Azure DevOps. Only items that are approved in ADO are deployed to production.
Given this setup, does anyone know if it's possible to implement incremental refresh in the QA environment (already using production data) and then deploy it to production? Or is it mandatory to configure incremental refresh directly in the production environment?
What I want to avoid is making any changes directly in the production workspace. Ideally, developers should not have access to the production environment.
Thanks in advance!
Thank you, rohit1991, for your response.
Hi bdpr_95,
We appreciate your inquiry sent through the Microsoft Fabric Community Forum.
Based on my understanding, you can configure and validate incremental refresh within your QA environment. The refresh logic defined using RangeStart and RangeEnd is embedded in the dataset’s metadata and will be retained when deployed to the production environment via your DevOps pipeline.
While it is considered best practice to use production like data in the QA environment to ensure realistic validation, it is not mandatory. Once deployed to Production, you may update the data source or parameters, and Power BI will automatically perform a full refresh using the new production data source. Thereafter, the incremental refresh policy will be applied from that point onwards.
Additionally, please find below the relevant links for your reference:
Incremental refresh for semantic models in Power BI - Power BI | Microsoft Learn
Configure incremental refresh for Power BI semantic models - Power BI | Microsoft LearnWe hope the information provided will assist in resolving your issue. Should you have any further queries, please feel free to contact the Microsoft Fabric community.
Thank you.
3 Replies
Hi bdpr_95 ,
Yes, you can configure incremental refresh in your QA environment (using production-like data) and then deploy it to production.
The incremental refresh settings are defined in the dataset’s Power BI model and will carry over when you publish to the production workspace via your deployment pipeline or DevOps process.
You do not need to reconfigure incremental refresh directly in production, just make sure your parameter values and refresh ranges are set correctly before deploying.
But I need to make sure that, in the QA environment, when I’m setting up incremental refresh, I already have production data available, correct?
Or is it possible to implement incremental refresh in QA, and then, once I move to production, simply update the parameters, and Power BI will automatically “restart” the incremental refresh using the new data layer?
Thank you, rohit1991, for your response.
Hi bdpr_95,
We appreciate your inquiry sent through the Microsoft Fabric Community Forum.
Based on my understanding, you can configure and validate incremental refresh within your QA environment. The refresh logic defined using RangeStart and RangeEnd is embedded in the dataset’s metadata and will be retained when deployed to the production environment via your DevOps pipeline.
While it is considered best practice to use production like data in the QA environment to ensure realistic validation, it is not mandatory. Once deployed to Production, you may update the data source or parameters, and Power BI will automatically perform a full refresh using the new production data source. Thereafter, the incremental refresh policy will be applied from that point onwards.
Additionally, please find below the relevant links for your reference:
Incremental refresh for semantic models in Power BI - Power BI | Microsoft Learn
Configure incremental refresh for Power BI semantic models - Power BI | Microsoft LearnWe hope the information provided will assist in resolving your issue. Should you have any further queries, please feel free to contact the Microsoft Fabric community.
Thank you.
