u/DropMaterializedView

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.

So, to start this off, I do not really have experience working with any kind of real-time data, but I have been wanting to learn, so I have been on the hunt for a project...

My initial pitch to my wife was that I buy some motion sensors and a camera to model the behavior of the raccoon that has been digging holes in our backyard and predict when it will appear every day. The idea was simple... Motion sensor turns on camera, which captures photo, sends data to Fabric, which classifies it as a raccoon and feeds data into a simple ML model that would eventually predict raccoon arrival time...

But the sensors and camera were going to be about $100, and that was more than I was willing to spend. So, I started looking around my house for other sensors we might already own.

Turns out for a $5 payment you can access the google smart device API which meant I could stream events off my Nest thermostat. So, anyway now I have my houses temperature and humidity data coming into Fabric. What should I do next?

Here is a video of my journey so far: https://youtu.be/jD7scobwIo0

The majority of time was spent trying to get GCP authentication working...which for someone who has never used google cloud was a real struggle.

So, I am still figuring out best practices and learning this piece of Fabric, but I am really liking using Fabric Plan as an easy way for Business users to maintain data that they would typically maintain in Excel in Fabric. It is kind of the best of both worlds... the business user gets an easy-to-use UI that they can even load data to by importing excel files, and we get nice data with change logs in place landed in a SQL database that you can just grab in to Power BI.

This is something I saw Khaled Chowdhury post about on LinkedIn and it is great!

Here is a video as to how you can set up: https://youtu.be/8zXVPGLQlcQ

I am sure I made a mistake or two along the way, but it is really pretty simple (and yes, you need Microsoft Fabric for this).

I keep seeing a lot of comments on here around token cost and data privacy when it comes to LLM use for Power BI and Fabric development...

However, I don’t think that either of these are real, major blockers. To prove that out, I got the Power BI MCP server working with an Nvidia model running on a 6-year-old 2020 M1 mini with just 8 GB of RAM that you can currently buy off eBay for ~$300-400.

Video demo of the MCP Server Running + how I set this up here: https://youtu.be/SMJP_-Z6-n0

Does it run well? No, it does not ... BUT... the only reason it is not running well is because I am hosting the model on a 6-year-old mac mini with 8gb of RAM.

There are laptops already on the market already for ~$3,500 that have 128 GB of RAM using AMD's new Unified Memory Architecture that I suspect could easily run all these AI tools completely locally and are an easily justifiable purchase for a large company as most employee laptops already cost a couple of thousand.

Not to mention the new surface ultra: Surface Laptop Ultra: The new performance Surface Laptop | Microsoft Surface

I have been working on manually building Fabric Data Apps, and I have spent the past few days working on creating interactions between Vega Visuals and the Data grid -- currently it is a bit tricky... and I keep on messing up setting up all the interactions by hand.

Wondering if anyone has solved this and if I am thinking about this the right way?

After the fifth time getting a visual interaction slightly wrong, I turned to my LLM and I asked it to build me a reusable module -- I have not gotten around to trying to implement / test what it built, as it is late here but wanted to share: repo

Still working away on learning how to build fabric apps by hand (no AI)

A few of things I learned between this video and my first that I show off in the video:

1. How to map metadata to my Semantic Model DAX Query via a Fabric Data Table
2. How to use the Fabric Data Grid instead of a HTML Table (this was big win)
3. The difference between a TS and a TSX file (got stuck on this for like 15 minutes.....)

You can watch the video here: https://youtu.be/hkrghb8qJQM

You can see the playlist with the first video here: https://www.youtube.com/playlist?list=PLIrfECpBFaB1Cf31oleUMu5vl2w6OfT9b

For the record, I really like using AI and I think people are going to build amazing things with Rayfin + AI tools. But like many of you, I am concerned about publishing something to a production environment that I couldn’t build by hand on my own. So, I am on a bit of a journey to learn how to code Fabric apps by hand. This is going to be difficult for me as they are web apps, and I don’t know anything about Typescript or React. But I was able to figure out how to make a simple app that displayed data from a Semantic Model in a table in ~2 hours and I recorded a video building it here: https://youtu.be/J2dfM_gLwaE

This is the first video in a series where I plan to make where I struggle through building these completely on my own (without AI) … let me know your thoughts and would love for you to come learn with me!

I built a Microsoft Fabric Notebook widget that uses semantic-link-labs to deploy Fabric + Power BI items through a standard Power BI Deployment Pipeline, with the added benefit that the widget can run Python-based tests.

It is probably a bit glitchy as it was vibe-coded in a day, but it's really nice to have a method to set up automated deployment tests using the standard Power BI Deployment Pipelines without having to have a full-on Azure or Git pipeline set up, so if someone wants to take and run with this idea I would be really happy (hint, hint Microsoft Friends 😉).

Notebook is here if you want to check it out.

Video where I talk about pipelines, this solution, and speed run setting up a full Azure Pipeline that can run Fabric Notebooks here.

I made this video on some fun things that you can control if you use: TMDL Mastery: Accessing Power BI's Hidden Developer Settings

In case you want to completely skip over the video here is the summary of what was covered :

---------

WARNING: some of these are likely not officially supported as they are generated by PBI internally and so as a result could change at any time

---------
Custom Power Query Query Sort Order

annotation PBI_QueryOrder = ["Fact Table","Dim Date","Table"]

Analyze in Excel Drill thru for a measure (good SQL BI Article on this: https://www.sqlbi.com/articles/controlling-drillthrough-in-excel-pivottables-connected-to-power-bi-or-analysis-services/)

detailRowsDefinition =

SELECTCOLUMNS(

'Fact Table',

"Order Date", 'Fact Table'[OrderDate],

"Customer", 'Fact Table'[CustomerName],

"Amount", 'Fact Table'[order_val]

)

Date Column Join Behavior

joinOnDateBehavior

Custom Annotations

Enabling or disabling Time Intelligence

annotation __PBI_TimeIntelligenceEnabled = 1

Discouraging Implicit Measures

discourageImplicitMeasures: true

So long story short, I was playing around prepping a demo on semantic link labs and figured out you could:

• use semantic link labs to pull data out of a model
• Fabric Notebook AI functions to process the data and return it in JSON
• Semantic link labs to write the JSON results back to the model

I put together some fun examples in a notebook on my github here

Youtube video Demo here: https://youtu.be/-ky-JeH9bsI

Demos are as follows:

• Demo 1: Parsing SQL Tables from M Code
• Demo 2: Standard Power BI API to generate AI Measure Descriptions
• Demo 3: AI Measure Descriptions with a custom prompt
• Demo 4: Auto-Organizing Measures into Folders

I think a more involved real world use case of a pattern like this would be:

• having a notebook loop thru all the semantic models in a workspace / tenant
• use a custom prompt + similarity function to identify all the measures that should be the same + named the same definition
• Flag the deltas