Forum Discussion
Semantic model load limit 5 hours Power BI
Hello, I have a problem.
I have a Power BI project in which I configured incremental loading. However, when I publish it to the Power BI Service, a new semantic model is generated, and the incremental load only works from the second run onwards, since the first run was a full load.
The problem is that this full load takes more than 5 hours and is canceled. Is there a way to initially load all the data into the semantic model (full load) and then apply the incremental load without exceeding the 5-hour limit?
My table has 342 columns and 20 million records.
I know there are ways to optimize the model by reducing columns or data, but they require all the data to be loaded as is. I have Microsoft Fabric; please, I would appreciate your support.
Please see this bootstrapping, on booting loading models.
You publish. Manually activate the refresh policy. Then manually refresh each partition, to avoid the full refresh on first deployment
Hi cristianj23a,
Thanks for reaching out to the Microsoft fabric community forum.Scheduled refreshes for imported semantic models time out after two hours. This time-out is increased to five hours for semantic models in Premium workspaces. If you encounter this limit, consider reducing the size or complexity of your semantic model, or consider refactoring the large semantic model into multiple smaller semantic models.
Another method you can try With Premium capacities is refresh operations performed through the XMLA endpoint which have no time limit. To learn more, see Advanced incremental refresh and real-time data with the XMLA endpoint in Power BI - Power BI | Microsoft Learn
As you have mentioned it is not possible to decrease the data load, Please refer the above document and try to implement refresh through XMLA endpoint with which you can bypass time limit.
If you find this post helpful, please mark it as an "Accept as Solution" and consider giving a KUDOS.
6 Replies
- Akash_Varuna
Super User
Hi cristianj23a You could try to use Dataflow to manage the large data to chunks and then load please try these
Use Microsoft Fabric: Load historical data into Lakehouse or Dataflows in manageable chunks (by year or something). Configure Power BI to pull these chunks and enable incremental refresh for future updates.
Split Historical Data in Dataflows: Break the data into smaller partitions using Power BI Dataflows, ensuring the first refresh completes within the time limit. Use incremental refresh for subsequent loads.
- Try to reduce the complexity of data model for example by pre-aggregate data in Fabric or the source system, and simplify relationships to reduce load time.
- Deku
Please see this bootstrapping, on booting loading models.
You publish. Manually activate the refresh policy. Then manually refresh each partition, to avoid the full refresh on first deployment
v-nmadadi-msftCommunity Support
Hi cristianj23a,
Thanks for reaching out to the Microsoft fabric community forum.Scheduled refreshes for imported semantic models time out after two hours. This time-out is increased to five hours for semantic models in Premium workspaces. If you encounter this limit, consider reducing the size or complexity of your semantic model, or consider refactoring the large semantic model into multiple smaller semantic models.
Another method you can try With Premium capacities is refresh operations performed through the XMLA endpoint which have no time limit. To learn more, see Advanced incremental refresh and real-time data with the XMLA endpoint in Power BI - Power BI | Microsoft Learn
As you have mentioned it is not possible to decrease the data load, Please refer the above document and try to implement refresh through XMLA endpoint with which you can bypass time limit.
If you find this post helpful, please mark it as an "Accept as Solution" and consider giving a KUDOS.- v-nmadadi-msft
Hi cristianj23a,
May I ask if you have resolved this issue? If so, please mark the helpful reply and accept it as the solution. This will be helpful for other community members who have similar problems to solve it faster.
Thank you.
- v-nmadadi-msft
Hi cristianj23a,
I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions. If our responses has addressed your query, please accept it as a solution and give a 'Kudos' so other members can easily find it.
Thank you. - v-nmadadi-msft
Hi cristianj23a,
As we haven’t heard back from you, we wanted to kindly follow up to check if the solution provided by the community members for the issue worked. If our response addressed, please mark it as Accept as solution and click Yes if you found it helpful.
Thanks and regards
