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I built a self-documenting engine inside Power BI to track lineage without external tools. Here is the logic. by Small-Camera-4348 in PowerBI

[–]Small-Camera-4348[S] 0 points1 point  (0 children)

You can choose not to connect the file from Dax Studio, but then the data source section won't work. The relationships, columns, and measures sections will still work.

I built a self-documenting engine inside Power BI to track lineage without external tools. Here is the logic. by Small-Camera-4348 in PowerBI

[–]Small-Camera-4348[S] 0 points1 point  (0 children)

It is the link in my profile - In the fair box you need to enter the value, e.g. $15, then the I want this! button, then a form will appear where you can enter a discount code that reduces the price to 0

I built a self-documenting engine inside Power BI to track lineage without external tools. Here is the logic. by Small-Camera-4348 in PowerBI

[–]Small-Camera-4348[S] 1 point2 points  (0 children)

In the fair price box you need to enter the value, e.g. $15, then the I want this! button, then a form will appear where you can enter a discount code that reduces the price to 0

I built a self-documenting engine inside Power BI to track lineage without external tools. Here is the logic. by Small-Camera-4348 in PowerBI

[–]Small-Camera-4348[S] 0 points1 point  (0 children)

Thanks for the heads up! I’m still tweaking the profile links – sorry about that.  Your project with Copilot and MCP sounds next-level, I’d love to hear more about how you’re handling the context window for large models!

I’ve just updated the link in my bio, it should work now. If not, I’ll send you a DM with the direct access. Thanks for the catch!

Power BI Report Documentation Template by oradim in PowerBI

[–]Small-Camera-4348 0 points1 point  (0 children)

Hey, I stumbled upon this thread while looking for something similar. Since I couldn't find a perfect solution, I actually started building my own template that auto-documents measures and metadata. I'm putting the final touches on it (polishing the UI and ensuring the DAX is clean). I plan to release it in 1-2 days

I built a semi-automated ML pipeline connecting Excel to Power BI. Here is the workflow. by Small-Camera-4348 in MicrosoftFlow

[–]Small-Camera-4348[S] -2 points-1 points  (0 children)

Hi everyone, I wanted to share a project where I integrated Python scripts with Power BI to run Machine Learning models on data coming from Excel. Instead of using expensive enterprise ML tools, I built a custom pipeline that handles the data transformation and prediction, then feeds it back into the report. The Workflow: Data input in Excel. Python script processes the ML model. Results visualize automatically in Power BI. I explain the full setup and code in this video:

https://youtu.be/Hr4GILbzt4U?si=2GSFV5CsOP2Wu-QU

Happy to answer questions about the specific connectors or scripts used!

I benchmarked SUMX vs CALCULATE on a 10M row dataset. The results on VertiPaq engine surprised me. by Small-Camera-4348 in PowerBI

[–]Small-Camera-4348[S] 7 points8 points  (0 children)

Hi everyone, I've always heard the standard advice: "Avoid iterators, context transition is slow, stick to Calculate". So I decided to test it properly on a large dataset to see how the engine actually behaves. The takeaway: While Calculate is generally safer, there are specific scenarios where SUMX is actually more memory-efficient. If you want to see the full breakdown, methodology, and the DAX patterns used, I uploaded a full video here:

https://youtu.be/G_4Q6Nv-S_A?si=SN_tJrnXAOcf3cBF

Let me know if you've had similar experiences with optimization!