Don't miss your chance to take the Fabric Data Engineer (DP-700) exam on us!
Learn moreNext up in the FabCon + SQLCon recap series: The roadmap for Microsoft SQL and Maximizing Developer experiences in Fabric. All sessions are available on-demand after the live show. Register now
Hi, I hope everybody is good 😎
New to Power BI.
Does anybody have any experience of using behaviour study groups in Power BI. I need to filter my model based on a customer behaviour analysis - example: % of sales over a time period etc. The dataset is large and so my gut is to run the analysis during the refresh and then use the classifications to filter the model. However, I am not entirely certain how to model the table to constrain both the customer and date dimension? or alternatively attach it directly to the fact table?
Any thoughts would be greatly appreciated.
Solved! Go to Solution.
Hi @bi_analyst_2024 ,
Regarding your question, when building a model, it is best to design it according to star architecture principles. That is, there is a one-to-many relationship between multiple dimension tables and a fact table.
I hope this article has been helpful to you.
Model relationships in Power BI Desktop - Power BI | Microsoft Learn
Best Regards,
Wenbin Zhou
Thanks for your response. Sure, I am aware of that. Interested in anyone's experience of using behaviour groups, specifically.
Hi @bi_analyst_2024 ,
Regarding your question, when building a model, it is best to design it according to star architecture principles. That is, there is a one-to-many relationship between multiple dimension tables and a fact table.
I hope this article has been helpful to you.
Model relationships in Power BI Desktop - Power BI | Microsoft Learn
Best Regards,
Wenbin Zhou
If you have recently started exploring Fabric, we'd love to hear how it's going. Your feedback can help with product improvements.
A new Power BI DataViz World Championship is coming this June! Don't miss out on submitting your entry.
Share feedback directly with Fabric product managers, participate in targeted research studies and influence the Fabric roadmap.
| User | Count |
|---|---|
| 49 | |
| 44 | |
| 42 | |
| 19 | |
| 18 |
| User | Count |
|---|---|
| 72 | |
| 66 | |
| 33 | |
| 32 | |
| 31 |