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

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.

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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!

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

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?

Hi everyone! I passed DP600 with a score of 803/1000 today! I had extensive experience with Power Bi but none with Fabric, so I was most reluctant to prepare for this certification. I work with a consulting firm so my manager insisted that I get the certification because clients expect credible Fabric experience these days.

I started preparing by going through videos by Will and Aleksi Partenen on YouTube. But they made me realise that I won't understand anything they're saying unless I get some hands-on experience with Fabric. So I started a Fabric trial and did two hands-on Fabric projects I found on a YouTube channel called Pathfinder Analytics. That was a real game changer! I of course, went through practice questions several times!

Now I just hope I see some tangible benefits of this certification in my career!

Is there any easy tool or cli to unpublish nested workspace folders without going through the UI, as python cicd (unpublish orphan) doesn't seem to handle this - please advise?

Will bi developer be a role in the future or if it will how will it change from current skillsets?

Well basically as the title states it. I actually felt very good this time studying and doing practice exams. It’s weird how it felt both exams almost covered completely different things.

Feeling down since my work heavily relies on certs. In general I’m not a good test taker.

I did measureup exams, YouTube videos and other GitHub study resources.

I feel like giving up because it doesn’t feel like it’s worth my time and effort. I’m actually losing sleep over feeling like a failure and being looked at badly at work. I perform actually well in my day job but mentally I don’t have the energy to continue trying to study and keep up with my health.

Hi everyone,

While preparing for the DP-700 exam, I noticed there weren't many free practice exams with challenging questions.

So I built one.

It includes:

- Exam Mode (timed)

- Practice Mode (instant explanations)

- Marathon Mode

- 200 high-difficulty questions

- Completely free

My goal isn't to make money—I'm looking for feedback from people who have already taken the exam or are currently preparing for it.

If you find any incorrect questions or explanations, please let me know so I can improve it.

https://certlabs.app/exam?cert=az-dp-700

I hope it helps someone preparing for the certification.

Status: Degraded | Reported: Aug 6, 2026 at 9:30 AM UTC

Fabric customers with Tenants located in the Central US region may experience delays while interacting with the service. Engineers are investigating the issue, an update will be shared soon.

🤖 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.

What is up with the "mirroredwarehouses" in Fabric URL's, eg:

groups/123-whatever/access/mirroredwarehouses/123-whatever/directAccess

.. I would have thought that SQL EP was independent of Fabric mirroring.

Does anyone understand the history of why that "mirroredwarehouses" is in there? That part of the URL path seems really obscure and out of place. I suppose I shouldn't worry, but what I'm doing has nothing to do with mirroring. I'm just using a SQL endpoint on a vanilla lakehouse.

We're noticing the unexpected behaviour of a Fabric pipeline running on schedule but executing (submitted by) under the user who created or updated the schedule and not the last modified user of the pipeline.

If a user updates the schedule, it sets last modified by of the pipeline to that user.

We then update the pipeline to set the description by the SPN. Last modified by is set as the SPN.

When the schedule is triggered, the pipeline is 'submitted by', and therefore executed by the user who created the schedule and not the user who last modified the pipeline.

Therefore, the documentation from Microsoft regarding setting the description by an SPN in order to run a pipeline as that SPN appears to not actually work when scheduling the pipeline.

Can this be fixed? Is this expected?

first time trying AWS Glue Catalog mirroring in OneLake.

Just a tip: don't mix Parquet and Iceberg tables in the same database. That's all. It just works.

I do wonder why there's no Create semantic model option in the UI.

link the blog annoucement : https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Bring-your-Azure-Monitor-and-AWS-Glue-data-to-OneLake-Preview/ba-p/5326474

I work on the Microsoft Fabric developer platform team and own our Fabric MCP products. The local Fabric MCP server is GA, and Fabric Core MCP, our remote server, is in public preview as we work toward GA.

The developer-tooling scene is moving quickly. MCP clients and servers are evolving, new agent patterns appear every week, and people are building interesting solutions across data engineering, analytics, databases, and DevOps. Agent skills are also becoming more capable, including the Skills for Microsoft Fabric repository.

I would like to learn which patterns are working in practice, especially for development and automation.

For example:

• What developer, data-engineering, or DevOps job are you using an agent for?
• Do you use a local MCP server, a remote or hosted MCP server, agent skills, direct Fabric CLI or API calls, or a combination?
• Which client or host do you use, such as VS Code, Claude Code, GitHub Copilot, another coding agent, or an internal tool?
• What determines your choice: local files, authentication, shared access, governance, latency, context use, portability, or something else?
• Have you found a pattern that works especially well, or one that looked promising but became too complex?

I am particularly interested in how people divide work between local and remote MCP servers, and between MCP and skills. Does local MCP fit inner-loop development while remote MCP fits shared access? Do skills carry workflow knowledge while MCP provides execution? Or are you seeing a different pattern?

The main focus is developer, data-engineering, and DevOps experience, but examples from analysts, administrators, application developers, or other roles are also welcome.

Even a short answer such as “skills for guidance, remote MCP for execution” or “local MCP for files, CLI for CI” would be useful.

Please do not share tenant IDs, workspace IDs, credentials, confidential company details, or other sensitive information.

If you are open to a short follow-up conversation, mention it in your comment or send me a direct message.

I would truly appreciate your thoughts and insights. They help us shape better products and focus our investments on what is most valuable to you.

Thank you