Don't miss your chance to take the Fabric Data Engineer (DP-600) exam for FREE! Find out how by attending the DP-600 session on April 23rd (pacific time), live or on-demand.
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
I want to analyze this data other than a typical market basket analysis.What and how should I do that? Any suggestions?
Solved! Go to Solution.
Hi @abhijsrwala ,
For different data models, you can combine the business requirements by creating different measure or calculated columns to achieve. For example, refer to the following test: results based on different groupings:
M_1 =
CALCULATE (
SUM ( 'Table'[E] ),
FILTER ( ALL ( 'Table' ), MAX ( 'Table'[Emp_id] ) = 'Table'[Emp_id] )
)M_ = CALCULATE(SUM('Table'[E]),ALLEXCEPT('Table','Table'[Name]))
In addition, powerbi also provides many template examples for reference.
Tutorial: Explore a Power BI sample - Power BI | Microsoft Docs
If the problem is still not resolved, please point it out. Looking forward to your reply.
Best Regards,
Henry
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @abhijsrwala ,
For different data models, you can combine the business requirements by creating different measure or calculated columns to achieve. For example, refer to the following test: results based on different groupings:
M_1 =
CALCULATE (
SUM ( 'Table'[E] ),
FILTER ( ALL ( 'Table' ), MAX ( 'Table'[Emp_id] ) = 'Table'[Emp_id] )
)M_ = CALCULATE(SUM('Table'[E]),ALLEXCEPT('Table','Table'[Name]))
In addition, powerbi also provides many template examples for reference.
Tutorial: Explore a Power BI sample - Power BI | Microsoft Docs
If the problem is still not resolved, please point it out. Looking forward to your reply.
Best Regards,
Henry
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
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 |
|---|---|
| 48 | |
| 45 | |
| 41 | |
| 20 | |
| 17 |
| User | Count |
|---|---|
| 69 | |
| 64 | |
| 32 | |
| 31 | |
| 27 |