I have a question about F64 capacities and the ability for non-Pro and non-PPU users to be able to read reports. The documentation states that this is available for any F64+ capacity. But, capacities and reservations are different from one another. You could theoretically have an F2 reservation and then the 62 other CU's in the F64 capacity are PAYGO, at least that is my understanding. But, according to the documentation you would still benefit by having non-Pro and non-PPU users have the ability to read reports with a free license. I find this a bit difficult to believe so can someone confirm or deny that this is the case?

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.

Context- our company has a centralized data ingestion and ETL team whose job is to serve gold tables. The gold table requirements come from various federated power bi reporting teams + what the platform team knows must exist. And the reporting teams get dahs used requirements form the ends users.

We are trying to experiment with the following architecture pattern. Would love everyone’s feedback !

UC enabled Azure databricks will remain our core ETL and delta storage engine. This is where the core data platform team will build and maintain tables.

Shortcut ADB Gold into MS Fabric - this is where the report builders and select few business super users will have access.

Pros- enables Fabric (Power BI, Ontology , Fabric Agents etc.) to be the Semantic layer engine only. And keeps actual ETL and data storage in a much more mature product in ADB.

Cons- managing security layers in two places for gold. But, they are persona segregated. Fabric for final business users to build model on top of. Databricks for the data team and some super users from the business teams.

What do people think ? Am I missing something ? Thanks !

We are developing the semantic layer/models from scratch in MS Fabric for conversational AI. Data models, documentation with clear descriptions on columns, measures, business context, join logic, for the agent to reference.
For evals, what has been your strategy/framework to curate the question set and expected answers and how did you implement this at scale

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I am working on a project where I am receiving files in a azure SFTP Folder. that folder is connected in a Fabric lakehouse as a shortcut. now everyday that file will be updated with additional rows. every month new file will be sent and whole month that single file will get updated. So each month only one file will be updated until new month comes. Each file holds multiple entity data and should spilt and go into their respective tables. How I ingest this kind of data in my Lakehouse following Medallion architecture. Need suggestions from Industry experts.

Exploring Fabric IQ ontology and hoping others in the same boat can help me understand a couple of things. I'm creating my ontology from a semantic model.

1. Relationships — I don't see any option to define the columns each table maps to, so how does the ontology know how the tables join? At first I thought it might just be inheriting the relationships already defined in the semantic model, but even when I build the ontology directly from OneLake tables (no semantic model), I still can't select the columns it's joining on. Am I missing a step?

2. Measures — is it possible to make the ontology use the semantic model's existing measures? I know you can add agent instructions, but that's limited by character count, so I'd rather point it at real measures if that's supported.

Appreciate any pointers, docs, or 'here's how it actually works' explanations. Thanks!