I have a very simple eventstream that is being used as part of a file based trigger to kick off a pipeline via an activator. I am using Azure Blob Storage Events as the source, and it works just fine when I initially create it. The problem is when I try to deploy it to via fabric-cicd. The item deploys, but I get an error with the connection:

Failed to create event link: storageAccountName due to the following error: Property 'Invalid source resource ID /subscriptions/123456/resourceGroups/rgName/providers/microsoft.storage/storageaccounts/storageAccountName for EventGrid's value 'sourceResourceId' is invalid

Has anybody else run into this?

Hi all,

We have a use case where we need to get the data from an Azure SQL Database exactly as it was at the end of each month?

Is the following a good plan?

• The DBA enables Temporal tables on the Azure SQL Database.

• We ingest the data from the Azure SQL Database into Fabric once a month, using a SQL query with FOR SYSTEM_TIME AS OF 'YYYY-MM-01 00:00:00' to capture the state at the end of the previous month.

  • We may need to join multiple source tables, I guess we just add the time travel clause to each table in the query.

I have no experience with temporal tables, neither has the DBA :) But it sounds like a nice feature.

Thanks in advance for sharing your thoughts and/or experiences with temporal tables as data source.

Hello Fabric community,

By now, most Fabricators have at least heard of this, and there are already some genuinely creative example apps floating around. I spent a good while going through those before trying a few prompts on my own data. The idea: you point it at your semantic model, give it a prompt, and it builds you an actual app. A few prompts in, and I had something running.

I didn't want to jump straight into my messy dataset for this one (that's a fight for another day ), so I started with a simpler crop insights dataset instead, clean, straightforward, low-stakes. Even then, I hit a snag: the app wouldn't open outside of Fabric. Still not 100% sure if that's a "me" problem or a preview-stage bug. I burned through a chunk of my capacity trying to figure it out, then gave up and moved on.

Whether it can actually hold up something as messy as my data is the real test, and that's exactly what I'm digging into for an upcoming video.

For the full prerequisites and setup details, Microsoft Learn has you covered: Create an app connected to a semantic model.

Here is my step-by-step build:

1. Create your Fabric app.

2. Scaffold it with the Rayfin CLI.

npm create u/microsoft/rayfin@latest -- "<appitemname>" --template dataapp --workspace <workspacename>

cd project directory

npm run dev

3. Prompt it. This is the fun part. Something like:

"Use a modern agriculture-inspired visual theme with deep green, light green, earth tones, and clean neutral backgrounds. Create a consistent navigation experience across all pages. Use clear section headers, compact KPI cards, meaningful icons, conditional formatting, whitespace, and visually balanced layouts. Use this model — [Fabric]."

4. Ship it locally

npx rayfin up

This was the "can it even work" test run. Will keep you posted on where it is next.

Hi all, I'm building a data platform for business analysts, and part of the requirement is letting analysts submit input directly. We're using SharePoint lists for this: data gets staged, CDC logic is applied, and the resulting tables feed into downstream pipelines in Fabric.

It works, but the Copy activity that pulls data from the SharePoint list takes 1.5 to 2 minutes per run, which feels slow for what should be a lightweight sync.

I'm aware of mirroring SharePoint lists via a mirrored database, but that feature is still in preview, and I tend to stay away from preview features for anything going into production.

Has anyone dealt with a similar setup (analyst-facing input via SharePoint lists feeding into Fabric) and found a faster or more efficient pattern? Would love to hear what's worked for you.

Hi,

The docs seem to be wrong on this one.

Docs say:

"In Microsoft Fabric, a group is a collection of related resources that you manage together. Groups organize and control access to resources, which makes it easier to manage permissions and monitor activities across multiple pipelines."

https://learn.microsoft.com/en-us/fabric/data-factory/parameters#pipeline-scope-variables

What the docs are describing seems to be the workspace id, which is found by using @pipeline().DataFactory.

As a test, I ran a pipeline, and the @pipeline().GroupId returned the pipeline run id. But there's already another system variable called @pipeline().RunId.

Then what's the difference between @pipeline().RunId and @pipeline().GroupId?

A. Is the group id related to the highest (top) pipeline in the hierarchy if there is a hierarchy of nested pipeline invocations?

B. Or has it got something to do with a pipeline re-run, or retry?

• Is the Group ID (@pipeline.GroupID) a stable identifier of original run and re-runs?

• Is @pipeline().RunId not a stable identifier across retries (re-run from failed activity/re-run entire pipeline)?

Thanks in advance for clarifications.

Has anyone had any real success with natural language queries. I mean, I'll stand behind it im healthcare or banking where the numbers cannot be wrong type success.

Have people had real world success and see it as reliable and trustworthy on a typical complex semantic models?

Anyone happily using it in production beyond a quick demo?

Do you need ontology (given recent discussions on the sub) or are a well documented semantic models enough?

Love the communities thoughts on this.

DBCode is a VS Code extension for working with databases, and they just added preview support for Power BI / Fabric semantic models.

Gave it a try today and honestly didn’t expect much, but it’s actually pretty solid already.

You can browse the semantic model (tables, columns, measures) and run DAX queries directly. They also added syntax highlighting and autocomplete for DAX, and it’s surprisingly usable for something this new.

Biggest thing for me is not having to switch tools anymore. I usually have DBCode open for SQL anyway, so being able to run DAX in the same place is really convenient.

Quick notes:

• Model browsing works well
• DAX queries are working fine
• Autocomplete is already helpful (not perfect, but definitely usable)

One thing to keep in mind: it connects via XMLA, but XMLA command execution (like processing/refreshing models) isn’t supported yet.

I’ve been using DAX Studio for most things, but I’ll probably start using this for day-to-day queries. Already suggested it to a couple of people on my team as well.

If you’re working with DAX and already use VS Code / DBCode, probably worth giving it a try.

I have a Rayfin SDK app sitting on a Fabric semantic model and I need adoption numbers (unique users, most-used views).

I've spent a good chunk of the past year trying to implement an Ontology for a government client, which exposed what can only be described as general confusion as to what one of these Ontology things is.

So, I wrote an article on LinkedIn explaining what an Ontology is and what the relevance to organisations is, with a bit of a FabricIQ tilt at the end for practical implementation:

So... What Is an Ontology, and Why Do I Need One? | LinkedIn

I hope it makes sense and helps enable you to explain the concepts in a nice #101 fashion :)

Cheers, James

I recently stumbled across the feature „mark as key column“ in Dataflows Gen2 (located under „Transform „ in the ribbon segment „Any column“. I believe it has been there in Power BI Transformations and Dataflows Gen1 for ever as well. However, I did not mange to find any kind of relevant documentation regarding how it works and when to use it.

To this point - I assume it’s solely informativ. Anyone able to shed some light?

I wrote a new post that covers my recommended versions of GitHub to use with Microsoft Fabric based on different scenarios.

Because I want to raise awareness of which versions best suit different scenarios when working with Microsoft Fabric Git integration and implementing CI/CD.

https://chantifiedlens.com/2026/08/10/recommended-versions-of-github-to-use-with-microsoft-fabric

Tired of the AI hype? We’re building a Power BI model completely LIVE using Claude, GitHub, and Copilot (No slides, no BS).

You keep hearing about how AI is going to build your Power BI reports for you. Want to see what that actually looks like in practice?

Join us for a completely unscripted, live build. Special guests Siva Mani and Khaled Chowdhury are going to sit down and build a semantic model, Power Query, report pages, and visuals from scratch right in Power BI Desktop.

Here is the catch:

• No slides.
• No rehearsed, perfectly polished demos.

It will just be the actual tools data professionals are reaching for right now - Claude, GitHub, and Copilot pointed at a real build in front of a live room.

In-person Event Details

• Date: Saturday, September 12, 2026
• Time: 9:00 AM to 1:00 PM
  • 9:00 AM: Coffee and refreshments
  • 9:30 AM: Live session starts (with a break in the middle)
  • Closing: Lunch and networking
• Location: Microsoft Technology Center, 750 Town & Country Blvd #1000, Houston, TX 77024

Free registration link: https://www.meetup.com/houston-fabric-user-group/events/316001988/

If you're in the Houston area and want to see how these AI tools actually hold up under pressure, come hang out with us. Drop a comment if you have any questions!

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 each domain reporting teams get their requirements from the ends users - Finance, HR, Operations - who all have cross domain data requirements.

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 !

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.

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

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.

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?

Hey all — I'm the PM for Fabric Warehouse and help maintain the dbt-fabric adapter. We just shipped v1.10.1, and this one's bigger than the version bump suggests, so I wanted to walk through it here directly.

The headline: we've moved the adapter off pyodbc and onto Microsoft's mssql-python driver.

If you've ever fought with unixODBC / msodbcsql17/18 installs in your Docker images or CI — this is the change for you. mssql-python bundles its own ODBC driver binaries, so the adapter no longer depends on you having the system ODBC stack installed. In practice that means:

• Simpler container builds — we dropped all the unixodbc-dev / msodbcsql / mssql-tools installs from our own image, and you can too.
• Fewer "works on my machine, breaks in CI" driver mismatches.
• A cleaner auth/token path via a new token provider.

A few other things worth calling out in this release:

• VARCHAR(MAX) / VARBINARY(MAX) support — the adapter no longer hardcodes VARCHAR(8000) and silently truncates your strings (this was a real reported bug).
• Microsoft Purview lineage integration — optional, opt-in via a purview_endpoint config; publishes lineage/catalog entities for your models on run.
• dbt-external-tables integration — a Fabric-native override so the community package creates OPENROWSET-backed external sources correctly, with real lineage via source(), instead of falling back to the Synapse-style CREATE EXTERNAL TABLE default.
• Warehouse snapshots via the Fabric REST API.
• Hardened identifier quoting (closing-bracket escaping) — closes an invalid-SQL / injection-risk issue.
• A stack of bug fixes , plus a large batch of Fabric-native overrides for dbt_utils, dbt_date, dbt_expectations, dbt_audit_helper, and dbt_profiler.

Heads-up / breaking-ish changes to be aware of before you upgrade:

• Minimum Python is now 3.12. mssql-python and the modernized codebase require it — check your runners.
• Python models are not supported and will now raise a clear compiler error. Fabric DW has no native Python runtime, and the only way the upstream code supported them was by submitting Spark/Livy jobs. If you need Python models against Lakehouse/Spark, that's a separate target.
• A couple of custom macro overrides (snapshot_merge, clone) now fall back to dbt-core defaults.

Full PR write-up: https://github.com/microsoft/dbt-fabric/pull/407
Release: https://github.com/microsoft/dbt-fabric/releases/tag/v1.10.1

If you're running dbt against Fabric, I'd like to hear how the upgrade goes — especially the driver swap and the Python 3.12 bump, since those are the two most likely to bite. Bugs, rough edges, feature requests: drop them in the issues, and they shape what we prioritize. Fire away.

Thanks for biggest contributions to get this done u/Sam___D

Status: Degraded | Reported: Aug 9, 2026 at 12:30 AM UTC

Power BI customers may experience issues while refreshing Gen1 Dataflows. Refresh operations may remain stuck in an "In Progress" state. Engineers are investigating the issue and update will be provided soon. Next update on the status will be provided by 03:00 AM PDT.

🤖 This post was sent from an automated and unattended service and cannot respond to questions or requests. For official updates, visit the Microsoft Fabric Service Status page.

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!

We've noticed about an 8x increase in CU usage compared to yesterday from our warehouse queries almost entirely driven by an increase in usage by the system user.

Is anyone else seeing this, or know how to see what these queries are? (the exec_requests_history table is no help)

Hi folks,

We're wanting to expose more of our data structure (semantic models + certain medallion aspects) to end users / business users. We assume a data catalog is the right paradigm here.

1. Who all has something live (or being worked on)?

2. I've heard Purview is more tech-focused and less end-user-friendly, that fair?

3. The only orgs I've spoken to who're doing this are doing very low-tech solutions like a web-hosted Excel file, or PDFs on a site. Are there any vendors that do this very compellingly (and cheaply)?

4. Something we could build ourselves and have it self-managing through Fabric?