Performance problem in power bi

[holgergubbels]

Hi all,

I have a performance problem in power bi:

I have subscriptions from salesforce and calculate the Monthly Recurring (MRR) revenue. We want to analyze the change from the previous to the current month. All Products a grouped in "domains".

- upsell in a domain

- downsell in a domain

- crosssell (new domain for an existent customer)

- product churn (customer is still customer but canceled a domain)

- churn (customer canceled)

- new customer

For every "class" I need the MRR and no of customers.

And we want to see the development for 24 months.

==> a lot of calculations

Because I had problems within visuals (timeout) I introduced a calculated table with all results for every month. The problem is, that now every change in the datamodel (new measure, measure change) starts a recalculation of this temporary table.

I would love to have this precalculated table somewhere outside - before power bi comes in to play. But at the same time I want to reuse my DAX-formulas. So I am looking for a kind of a datamodel, which understands DAX where I can precalculate my results. A little bit like a python notebook.

Any suggestions?

Holger

[V-yubandi-msft]

Hi @holgergubbels ,

As we haven’t heard back from you, we wanted to kindly follow up to check if the solution we provided for your issue worked for you  or let us know if you need any further assistance?

Your feedback is important to us, Looking forward to your response. 

[V-yubandi-msft · Accepted solution]

Hi @holgergubbels ,

Thank you for reaching out to the Microsoft Fabric Community. 

1. Consider moving part of your ETL and business logic (such as churn classification and MRR deltas) upstream into Power BI Dataflows or Fabric Data Pipelines.

Precompute metrics in Power Query (M).

Enhance report responsiveness by loading only aggregated data.

Creating a dataflow - Power BI | Microsoft Learn

Ingest data into your Warehouse using data pipelines - Microsoft Fabric | Microsoft Learn

2. For highly customized logic and scalability, consider moving calculations to a Python Notebook exporting the results as a table to a Data Lake or SQL database, and connecting to it in Power BI.

Develop, execute, and manage notebooks - Microsoft Fabric | Microsoft Learn

3. As your data grows over time, consider using incremental refresh to reduce the load on large date-based tables and aggregation tables to speed up visual rendering.

Incremental refresh for semantic models in Power BI - Power BI | Microsoft Learn

User-defined aggregations - Power BI | Microsoft Learn

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.