Forum Discussion
Semantic Model Refresh via Data Pipelines
Hi bdpr_95
When you assign deployment pipeline variables to control the workspace and semantic model connections, Power BI disables the “Select all” option under the table setting because the variable-based configuration replaces the static, pre-selected metadata binding. Essentially, variables introduce dynamic references that prevent Power BI from automatically resolving all table mappings at design time. Unfortunately, there’s currently no direct workaround to restore the “Select all” option when using variables. To manage this efficiently, you can either define the specific table mappings manually once per environment, or automate this process through the Power BI REST API or deployment pipeline API, which lets you script updates to dataset bindings and connections programmatically. While this adds a bit of setup, it ensures you can maintain a fully automated deployment process without having to adjust table mappings manually in each workspace.
v-tejrama , is there any possibility of creating a notebook that refreshes the semantic models one by one instead of updating all of them at the same time? The issue is that I have semantic models that need to be refreshed daily and others that only need to be refreshed monthly. So I always have to filter them first and then update the semantic models. Also, I’d like to refresh one semantic model at a time, not all simultaneously. And if possible, I’d like to receive some kind of notification via Teams or email if the process for semantic model X fails.
Hi bdpr_95 ,
You can definitely achieve that by creating a Fabric notebook that triggers the semantic model refreshes one at a time instead of running them all in parallel. Within the notebook, you can call the Fabric REST API to refresh each semantic model individually, and include simple logic to determine which ones should run based on your schedule so your daily models can refresh more often, while the monthly ones only run when needed. By looping through your model list and waiting for one refresh to complete before starting the next, you can fully control the sequence and avoid overlapping refreshes.
If you’d like to be notified when something goes wrong or when a refresh finishes, you can extend the notebook to send alerts through Microsoft Teams or email. This can be done using a webhook or the Microsoft Graph API, which lets you post a message or send an automated email when a refresh fails or succeeds. This approach gives you a clean and reliable way to manage refreshes with different frequencies and still stay informed without needing to monitor them manually.
Best Regards,
Tejaswi.
Community Support
Hi bdpr_95 ,
You can address this by setting up a Fabric notebook that uses the REST API to refresh your semantic models individually, rather than all at once. Within the notebook, you have the flexibility to list your models and define which should be refreshed daily or monthly, applying logic to run each refresh sequentially. This approach helps prevent overlap and potential performance issues.
For notifications, you may include a step in the notebook to send alerts when a refresh completes or encounters an error. Common options include a Microsoft Teams webhook or the Microsoft Graph API for automated email notifications. This ensures you receive timely updates on the status of each model refresh.
After validating the notebook’s functionality, you can schedule it using a Fabric data pipeline. This automates the refresh process for your daily and monthly models, keeping you informed without the need for manual intervention.
Thank you.v-tejrama thanks a lot for the detailed explanation! That sounds like a great approach. Could you please share an example of how the notebook code would look? For instance, I have reports A, B, and C that should be refreshed daily, and reports D, E, and F that are refreshed monthly. It would be super helpful to see how that logic could be implemented. Thanks again!