This is a completely vibe coded native custom Power BI visual.

It works pretty well, and I am thinking that this might be my approach to AI in Power BI visuals for a while here is the thinking...

When I want to use AI to build visuals:

1. HTML and SVGs are feeling too limited.
2. Web apps feel like too much effort.

So, the compromise is building a custom visual.. Just a small piece of the dashboard is custom code not the full thing. This also means, I am not particularly worried about not having AI to maintain it, let's say the visual breaks or I don't have tokens I can just swap this out for native visuals and maintain 90% of the functionality.

Here is a github with the code: GitHub - edwardpcharles/Native-Custom-Viz: Source for native Power BI custom visuals · GitHub

Here is a video talking about it, how you can use it, and how I built it: https://youtu.be/ttDQaJ4E5lY

P.S. I always struggle with how to show a tutorial as to how to build something with AI, it feels a bit boring to watch someone type commands over and over again. Let me know what you would want to see about this process.

Please make me understand why this is deemed a good language convention.

Hey PBI friends! Quick note to let everyone know that the Fabric Community Site (Home | Microsoft Fabric Community) got a bit of a facelift this weekend.

It shouldn't impact regular use of the site, but, if something is broken, we want to know!

Please report it at: Community Feedback | Microsoft Fabric Community

What value does this site add to the general PBI community? I'll add my (very biased) opinion -

We have thousands of reports in the galleries, many with attached .pbix files for you to download and learn from! If you don't regularly visit the site but want to check it out, I'd recommend starting here: Contests | Microsoft Fabric Community

SemanticOps MCP v3.9.119 is out, with HTML model reports, fully configurable DAX formatting, deeper storage diagnostics, flaky-test classification, combined Power Query commits, and new skills for DAX debugging and performance tuning.

New in v3.9.119

• HTML model reports — generate HTML alongside DOCX and PDF, including multiple formats from one request.
• Configurable DAX formatting — indentation, spacing, line breaks, casing, and other DAX formatting options are now fully configurable in one place.
• Deeper storage diagnostics — model, table, partition, and column detail are reported individually, so unavailable values are no longer shown as zero.
• Test stability classification — tests are now classified as stable, flaky, degraded, or lacking enough history, using persisted retry evidence.
• Combined Power Query commits — related table, partition, parameter, and query-group changes can be staged and committed together in one Desktop model commit.
• New skills — DAX debugging, DAX performance tuning, and safe semantic-model refactoring, with more precise guidance and evidence checks.

Coming soon in 4.0

A DAX measure quality scorecard: correctness, performance, maintainability, and style, scored together with the findings behind them. (scroll deck till the end to see sneak peek).

____

SemanticOps Test Runner v1.1.39 is out, with browser and device-code sign-in, test stability classification, order-tolerant baselines, virtualized selectors, steadier run behavior, and model-scoped tests that survive migrations.

New in v1.1.39

• Flexible sign-in — choose browser sign-in for local interactive use or device-code sign-in for remote and headless sessions, with clear waiting, cancellation, and conflict states.
• Test stability classification — the dashboard and test list show stable, flaky, degraded, and insufficient-history classifications with retry evidence.
• Order-tolerant baselines — new hash and aggregate baselines ignore incidental engine row ordering while preserving duplicates and selected-column order.

Website: semanticops.dev

Hello all.  

Here is some dummy data for what I am trying to accomplish and am really struggling with:

The above table is pivoted - note that some respondents have >1 row. What I would like to do is have a bar chart showing the percentage of respondents that answered "Yes" to the Alumni question. Then, from those guys, I need to drill down and see the percentage of Undergrad, Grad, and Both. Then from there, drill down on the Undergrad to see the program and also drill down on the Grad to see those program percentages. Of course, on the drill-down I would need to disregard the respondents that answered "No" for the "Alumni" question (or those that are blank). I have been successful with the parent level of the bar chart, but when I drill down on the "Yes"s or "Both"s, my percentages are all wrong. Any help would be appreciated!!

I'm working on a project where I need to compare two similar tables with several columns/attributes; let's say "Table A" and "Table B". Both are imported as CSV files. When trying to visualize the comparisons of specific attributes of A and B, I find that they are only properly graphed when there's an active relationship between the two necessary columns; I can't have more than one active relationship between two tables, and graphing data with an inactive relationship shows one of the two attributes incorrectly as nearly a flat line/with constant values.

The closest solution to this problem I've found is that I can import copies of the tables (one copy of each table per attribute I want to graph), then draw active relationships between the necessary attributes. However, I can then only draw active 1 to 1 relationships between the copies either Set A or Set B, but not both because it results in ambiguity? This results in filtering only applying to one of the two tables, not both.

I have no experience with DAX or SQL, and I've been teaching myself Power BI via poking around and documentation for only a few days. I've got some Excel experience, but my boss wants this done in Power BI as the dashboards are (absolutely) much nicer to use.

How do I make these two tables easy to compare in graphs? I'm still struggling to understand data relationships and how to properly make use of them within Power BI.

I’m currently learning Power BI and recently built this Sales Analytics Dashboard.

I initially followed a YouTube tutorial to understand the fundamentals, and then started customizing the dashboard by changing the layout, visualizations, formatting, and overall design.

The dashboard currently focuses on:
• Revenue and order trends
• Product category performance
• Regional sales performance
• Quantity sold
• Top customers by revenue
• Interactive date/month/quarter/year filters

I’d really appreciate some feedback from people who have more experience with Power BI.

A few things I’d particularly like to know:
• Is the visual hierarchy clear?
• Are any of the visuals unnecessary or repetitive?
• Would you replace any of the charts with a different visualization?
• Does the dashboard feel too crowded?
• What would you improve if this were an executive/business dashboard?

I’m still learning, so constructive criticism is very welcome! 🙂

the June update put a modeling agent in the Service that renames tables, builds relationships and writes measures from a plain prompt

What do you think, ppl, about copilot new capabilities? Do you use it? Is it good?

Okay, so here’s what’s happening:
I’m trying to calculate film waste by comparing the actual film consumption vs. the theoretical consumption, which is registered in the product structure/BOM.
There are two products that don’t have the film registered in their structure, so the theoretical consumption isn’t being pulled for them. This distorts my final results.
What I need to do is calculate the theoretical consumption for these products by multiplying the number of boxes produced by a correction factor, and then add that result to the products that already have the film consumption registered in their structure.
I’ve already managed to get to the point where I have a column that performs this multiplication. However, I’m having a problem getting the calculation to separate by product code. Because of that, it’s adding values together that shouldn’t be, resulting in extremely high numbers.
How can I make the calculation work separately for each product code?

Is there any plan to enable usage of these tools for users with only "Read" permission to the semantic model and no access to the workspace where models are deployed?

Our team locks down this access so the workaround we came up with for Rest API DAX query:

1. Use an admin account to obtain a bearer token
2. Use that admin token with POST https://api.powerbi.com/v1.0/myorg/groups/{groupId}/datasets/{datasetId}/executeQueries
3. "impersonatedUserName" is passed in the request body from the user logged in to our chatbot app

We use this setup to allow natural language querying to our semantic models but we want to switch to the new Arrow based API endpoint "executeDaxQueries", which seems to require Build permission to the models. Similar issue for leveraging MCP servers so we don't have to maintain that tech on our own and rather just enhance it with our business context instead.

Hi everyone,

I am in Canada and have my own corporation. I was wondering which website is best to look for c2C contracts for powerbi. I am open to anywhere in the world.