Power Query <> Dataverse very slow/crashing on parallels emulator

[Duncan_McGovern]

I'm running Power BI on a 2024 Macbook Air via emulator (Parallels) and finding it to be so slow as to be effectively unusable. The only thing I've done so far is hook it up to a handful of Dataverse tables (import, not direct query) and immediately strip out all but a handful of fields from each table. One or two tables have records in the hundreds of thousands (Contacts) but not huge data volumes. Contacts had enough columns and relationships that it would not load until I followed the guidance in this article to edit via advanced query and just pull in a handful of fields. Fine.

But just entering the 'Transform Data' UI takes minutes to load, trying to apply changes many minutes to process, adding new tables has crashed/frozen me out half a dozen times. I'm not doing ANY transformations other than dropping columns so can't figure out why it's so sluggish.

Are other folks using Parallels + Power BI successfully and this might be due to something in my configuration (if so, what)? Or should I give up trying to use Power Query on a virtual machine? It's not a particularly powerful computer, but it's pretty new and I've been careful to shut down any other apps. And when I use the Activity Monitor to watch CPU load, it's not doing much which leads me to suspect it's not a memory problem.

[Anonymous]

Hi @Duncan_McGovern 

Given the nature of your setup, there are a few areas we can explore to potentially improve the performance:

1. Optimize Power Query Performance: Since you're working with a significant volume of data, even with minimal transformations, it's crucial to ensure your queries are as efficient as possible. Utilize the Query Diagnostics tool in Power BI Desktop to analyze what Power Query is doing during data load and transformation. This can help identify any bottlenecks or unnecessary operations that might be slowing down the process.

2. Check Parallels Configuration: Running Power BI via an emulator like Parallels adds an extra layer of complexity, especially concerning resource allocation. Ensure that Parallels is configured to allocate sufficient CPU and RAM to the virtual machine. Power BI and Power Query can be resource-intensive, especially with large datasets. Adjusting these settings might improve performance.

3. Dataverse Considerations: When working with Dataverse, especially with large tables like Contacts, it's essential to be selective about the columns and relationships you're importing. You've already started doing this, which is great. 

If all of above don't help, you may consider using Azure Synapse Link to access large models. Using Azure Synapse Link is even more efficient than either the Power Query Dataverse or Common Data Service (Legacy) connectors, and is specifically designed around data integration scenarios. Find more from Alternative Dataverse connections section in Power Query Dataverse connector - Power Query | Microsoft Learn

Best Regards,
Jing
If this post helps, please Accept it as Solution to help other members find it. Appreciate your Kudos!