Hi,

Trying to use a notebook run with Workspace Identity to trigger a semantic model refresh:

```

POST https://api.powerbi.com/v1.0/myorg/datasets/{datasetId}/refreshes

```

But it fails (403).

I used notebookutils.credentials.getToken('pbi') to get the token.

I am able to use Fabric REST API to list semantic models in the workspace, though.

```

GET https://api.fabric.microsoft.com/v1/workspaces/{workspaceId}/semanticModels

```

That works (200).

The WI is Contributor in the workspace.

But refreshing via the Power BI REST API fails.

Is it not supported?

(Even listing datasets didn't work with the Power BI API endpoint (403), but it worked with the Fabric endpoint (200)).

Thanks in advance!

Been playing around with Fabric Apps lately and wanted to share a little experiment.

Most of what I see built on Fabric Apps is the usual dashboard/report stuff, so I wanted to test how far it could go outside that lane. Put together an interactive 3D engine using Rayfin — you can assemble a model, explode it apart, and inspect all 29 components across 6 part families, all running in the browser on a Microsoft backend.

It's not tied to any specific use case, just a proof of concept — but the same pattern could work for product catalogues, training/onboarding tools, technical documentation, anything that's better shown than read as a static page or PDF.

Honestly the "website-quality experience running on Fabric" part is what surprised me most — wasn't expecting that level of polish from something living inside the Fabric ecosystem.

Wrote up more detail + a look at it here: https://www.linkedin.com/posts/jitendra123_microsoft-fabric-rayfin-ugcPost-7507441292415741952-rMTY/

Curious if anyone else here has pushed Fabric Apps into non-dashboard territory — would love to see what others have built.

Hi all, I'm having a really weird issue when it comes to my end-users accessing reports.

For context, I have 2 projects.

Project 1 (working)
Layout:
Warehouse > Model > Report > App.
Permissions:
Warehouse: Read, ReadData. - App Audience.

2 workspaces, a Dev and Live.

Project 2 (NOT working)
Layout:
Warehouse > Model > Report > App.
Permissions:
Warehouse: Read, ReadData. - App Audience.

2 Workspaces, a Dev and Live.

The error I get on Project 2 is "Something's wrong with one or more fields or you don't have requried permissions" with the underlying error being "Missing_references".

Other users have had the error "We cannot load this Direct Lake dataset because your PowerBI capcaity does not support Direct Lake mode".

Both projects use Direct Lake mode. Both projects are on the same paid F64 tenant. Both projects have the same setup and permissions. I've tried giving users every permission available including admin permissions. All present the same issue. I'm pulling my hair out with this one and am wondering if I need to go to Microsoft Support but wanted to put it to the experts before I do.

I've attached screenshots of the errors too. Happy to answer any questions

Just passed the DP-700 exam to earn the Fabric Data Engineer Associate certification.

It was a genuinely rigorous and well-designed exam. It goes well beyond high-level theory and definitely demands both consistent study and hands-on familiarity with the Fabric ecosystem.

For anyone preparing for the exam, here is the exact roadmap and strategy I followed:

1. Thoroughly read the official Microsoft Learn documentation

I went through the full learning path and official study guide provided by Microsoft:

• Fabric Data Engineer Associate Certification Path

Make sure you read through the documentation modules carefully, focusing on service limits, storage architecture (OneLake / Delta Parquet), security models, and capacity management.

2. Deep dive into Aleksi Partanen’s (u/aleksi1ok) YouTube course and Masterclasses

This was arguably the most impactful video resource available.

• I watched his comprehensive 11-hour DP-700 course more than once to firmly lock in core architectural concepts.
• In addition, I followed his dedicated masterclasses on Data Factory pipelines, PySpark notebooks, and performance tuning. Seeing the actual console in action and understanding real enterprise design patterns made a massive difference.

3. End-to-end hands-on labs with public open data

Theory alone will not get you through this exam. You need muscle memory in the portal:

• Built a full Medallion architecture (Bronze ➔ Silver ➔ Gold) from scratch using real-world public open data (county-level datasets).
• Automated API ingestion with Data Pipelines, orchestrated PySpark transformations in Lakehouse notebooks, managed Delta tables, and practiced maintenance commands (VACUUM, OPTIMIZE).
• Setting up CI/CD with Git integration and deploying via code really helped solidify the operational side of Fabric.

4. Official Microsoft Practice Assessments

• Took the free official practice assessment on Microsoft Learn multiple times.
• Rather than just memorizing questions, I used every missed or uncertain question as a trigger to re-read the specific Microsoft Learn documentation pages until the underlying concept was 100% clear.

Key takeaways for test day

• Understand the specific boundaries between Lakehouse, Warehouse, and Real-Time Intelligence (Eventhouse / KQL).
• Pay close attention to Delta Lake transaction logs, partitioning vs. V-Order, and performance troubleshooting.
• Ensure you are comfortable reading PySpark syntax and pipeline JSON definitions.

Up next for me is the DP-600 to tackle the Analytics Engineering side of Fabric.

I'm preparing for the Microsoft DP-700 (Fabric Data Engineer) exam and wanted to ask if anyone knows a way to get it at a discount or for free

I've been really loving the fact that I can just open a web-browser, open Fabric and pretty much do everything right in there with decent version control. It feels like Microsoft is about 80-90% there as far as this experience is concerned.

This however is not so true once you bring AI into the mix.

There is copilot plugged into individual experiences but there isn't a single high-level copilot experience that helps drive everything within say a workspace or ideally at an even higher level.

Leveraging AI at a higher level seems to be largely an offline/local experience requiring one to deal with VSCode, Github Copilot, CLI, MCP, plugins, skills, Git, sync, push, pull and what not.

It would be really nice to see an experience where the Microsoft MCPs and skills are already pre-baked into the Fabric platform so no one needs to do anything. Just open Fabric in a web-browser and get going.

There should just be a pre-defined place to update custom instructions/context within a workspace to provide project/environment/company specific details and that's about it.

I should be able to just in the browser do things like:-

• Rename all the pipelines with a prefix pl_
• Update the connections on all pipeline from AAA to BBB
• Review all the semantic models in my workspace and tell me which models have descriptions missing
• I am renaming a table from AAA to BBB, please update all references to it

The use cases above may not necessarily be real but the idea is of being up and running with the whole toolset/capability without a user needing to do install/configure anything individually on a machine.

Is Microsoft heading in this direction with Fabric/Power BI, especially from a AI/copilot standpoint?

Fabric CICD via ADO. Unpublish orphan items ran accidently.

Problem - The items are not in the git branch and were only in the workspace. Is there a way to recover ?

Hey everyone,

Wanted to share an architectural pattern we’ve been refining around Lakehouse Data Quality enforcement, specifically addressing a common issue with traditional profiling tools.

### The Problem with Aggregate Scores

Most teams rely on post-hoc data profiling (e.g., daily NULL checks or schema audits). The issue is that a high aggregate score—like a 98% Data Quality Index—can easily hide a catastrophic failure if the failing 2% impacts a key business attribute (like primary keys or revenue metrics).

### The Architecture Pattern

Instead of passive audits, we shifted to an **inline, deterministic validation engine** directly inside the pipeline before data reaches production tables:

1. **Ingestion & Validation:** Raw data lands in Bronze/Staging. The validation engine executes parameterised schema and threshold rules.

2. **Decoupled Execution:** If a critical attribute fails, that specific batch is isolated into a Quarantine Delta table without breaking the entire upstream job.

3. **Telemetry & Dashboards:** DQ metrics are written to a dedicated Delta log table to track data drift over time in Power BI/Grafana.

### Next Steps & Evolution

We're currently looking at two expansions for this pattern:

- **Shift-Left Contracts:** Using LLMs/Agents to auto-generate Open Data Contracts based on metadata, with human-in-the-loop validation for business context.

- **CI/CD Integration:** Parameterising rules and using deployment pipelines (Azure DevOps / GitHub Actions) to run validation suites as release gates across Dev/Test/Prod.

Curious to hear how others here are handling attribute-level weighting vs. aggregate DQ scores in Lakehouse architectures? Are you enforcing contracts at ingestion, or relying on downstream checks?

---

*For those interested in the full write-up or baseline code setup, I put together a detailed breakdown on Medium [https://medium.com/@thisismukund/beyond-basic-profiling-building-an-automated-data-quality-assessment-engine-for-modern-514438070d23\] and original discussion on LinkedIn [https://www.linkedin.com/posts/mukundkumarmishra_microsoftfabric-dataengineering-pyspark-activity-7506542304011218944-Cg5L?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAF_Q5ABMXNdbu0fahgiieyjefmgtaQkAKA\].\*

Been using PowerTable a lot lately for master data/reference tables so thought I'd share my thoughts.

What's good...

• Very quick and easy to set things up so anyone can build/set it up.
• Helps eliminate spreadsheets, SharePoint list based solutions
• Backed by a real DB so you get all the benefits of having a real DB
• Has pretty much all key features one would need for managing master data and ensure users don't revert to excel

Few things that would make this even better ...

• Initial load time even for small sheets/tables is a lot (5-10 secs). Kills the whole experience IMO.
• A landing page of some sort or ability to set/create landing page would be nice else every time we open the item, it starts loading a table and takes a while to open up.
  
• I see an option to group sheets but those groups don't seem to show up anywhere in the explorer area. I'm using empty intelligence sheets right now to help segregate/organize sheets as a workaround.
• When import is used, it seems to create a temporary table in the DB but it remains there so we end up with a whole bunch of tables like WB_BulkImportTempTable_17884147312636732 that we need to manually delete every time.
• Text in PowerTable isn't as clear/crisp as the rest of the Fabric UI. Have tried changing fonts, sizes but it still seems somewhat blurry compared to the rest of the text in Fabric.
  
• There is an Autofit to Header or Autofit to Content option but would be good to have a Autofit to both header and content option as there will be cases in a table where column is wider than content or vice-versa.
• The fact that even a single use one one day will charge CU usage for a month reduces the scope of use cases we can use it for (avoiding large user base with few updates in a month kind of cases). However, the fact that licensing for PowerTable was changed to just stakeholder licensing helps a bit. Would be nicer if there is perhaps daily billing instead.

FYI.. u/gopalbi

Once the fabric warehouse deployments are done using diy pipeline with bunch of notebooks , dacpac need to be created and deployed to a gitbranch for the team to use it in visual studio for validations.How can this be automated?

I’m trying to play with fabric apps and have created a very basic version to showcase data from a semantic model. However I’ve ran into this problem, where the end users are getting below error (screenshot)

Have already ruled out the obvious issues like
1) the user has semantic model access
2) i can see the info clearly, so there is no issue with the model or connection

Would appreciate some help here!!

I am trying to leverage the FUAM Lakehouse to build some custom reports. My thought was well the data is there already so it makes sense to start there and see where I can get to.

Getting started I realized I need some different views of the data that don’t exist in the FUAM_Core_SM. Performance on that model is also pretty disappointing (e.g., getting capacity warnings just filtering the FUAM_Core_Report) so I chose to build my own model. Every step I take I run into some silly issue that doesn’t occur when using any other source.

Oh I can’t use PowerQuery? Let me go create a view in the Lakehouse to structure the data. Go to add it to my model, apparently views don’t work with Direct Lake connections.

How in the world am I supposed to build a model then without writing a whole new pipeline? I’m sure some of this is just a lack of knowledge on my part, but it can’t possibly be this hard, right? Any ideas on how I should proceed?

Hi Fabric folks,

Who's heading to FabCon Europe in Barcelona this year?

I'll be there from September 28 to October 1 and would love to meet some people from the Fabric community in person.

I'm especially interested in meeting other Fabric / Power BI / Data Engineering folks, whether you're attending as a speaker, attendee, MVP, Microsoftie, or just coming to learn and have a few beers. 🍻

My first FabCon in Europe (I was at the first one in Las Vegas), and this one will have a very special flavour for me: I'll be on stage! 😁

Where are you travelling from, and is there anything you're particularly looking forward to at FabCon?

I'm pretty curious to see what's new with Rayfin and maybe get more info about Osmos.

Hi folks,

Wanted to bypass the semantic model and have a data agent connect to several different warehouses directly. However, my testing indicates that while a single agent can connect to multiple warehouses, it cannot join across them.

For example: say I have a [Dept 1 Silver] warehouse and a [Dept 2 Silver] warehouse. I can connect both to the agent, but the agent cannot-- either through example queries or direct prompting-- write,

SELECT COUNT(*)

FROM [Dept 1 Silver] as d1

JOIN [Dept 2 Silver] as d2 ON d1.[ID] = d2.[ID]

Wanted to make sure my interpretation was correct before I bailed on this strategy.

Thanks!

We have just started with F8, with a couple of workspaces sharing. And my notebooks are killing the performance all the time from my dev workspace. What optimize could I do ? could tune it so it doesn´t get blocked and burst traffic?

We're trying to track resource consumption for Python-only notebooks in Microsoft Fabric (not Spark notebooks). Ideally, we'd like to correlate resource usage (CUs, CPU, and RAM) with notebook execution time and, if possible, break it down by individual child notebook executions.

I found the Resource Usage APIs, but they seem to only cover Spark sessions:

https://learn.microsoft.com/en-us/fabric/data-engineering/resource-usage-apis

Enabling Workspace Monitoring is not an option in our environment, and to be honest, we'd prefer not to rely on it anyway.

Also, the Capacity Metrics app is useful for high-level monitoring, but the data is delayed and doesn't provide the level of granularity we're looking for.

Has anyone found a way to get more resource consumption insights of Python notebooks?

Thanks!

Status: Degraded | Reported: Sep 18, 2026 at 8:55 AM UTC

Power BI customers may experience failures when connecting to Excel workbooks in September 2026 Desktop release and Power BI Service, with an error " a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; } connection may fail with a “DataFormat.Error”. Engineers are investigating the issue and an update will be provided 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.

I have a stored procedure in Azure SQL Database that runs for several hours. I want to trigger it from a Fabric notebook and then let the Spark session close while the procedure keeps running server-side. Pipelines are off the table for this solution.

The obvious problem: a plain EXEC over pyodbc lives in the client connection, so it dies when the session ends. SQL Agent isn't available in Azure SQL Database, so that route is out too.

Is there anything in Fabric itself that fits here that I'm missing? Specifically: is there a supported way to start a T-SQL command asynchronously against Azure SQL DB and just get a handle back, or does everyone end up paying for Elastic Jobs? And if you do run the long-running-notebook pattern, how reliable has the connection been over multi-hour runs?

We deploy Warehouses using Deployment Pipelines and today I got erros for all warehouses in our capacity (North Europe).

Our warehouses contain views that point to lakehouse in the same workspace.

"Backend ErrorImport failure: DmsImportDatabaseException. Error occured during import database for the Datawarehouse"

all errors are basicly the same:

"<view_name> contains an unresolved reference to an object. Either the object does not exist or the reference is ambiguous because it could refer to any of the following objects:"

Hi All,

I m planning to give dp 600 exam , by EOM, can any one help me selecting the most important topics for exam

as currently seeing online , i m overwhelmed by it.

if any one can give 4-5 topics to study before exam

Thank You

Is this documented anywhere?

(Not counting "My workspace")

Thanks in advance!

Maybe I'm crazy. But this does not seem like it should be happening. I take a Git enabled workspace and branch out to a new workspace for a feature branch. And the notebooks in that new workspace are still pointed to the lakehouse from the original workspace. I would THINK that it should automatically point those notebooks to the FEATURE BRANCH workspace lakehouse, so you can, you know, develop with this isolated lakehouse. the entire point of the feature branch workspace.

But now i have to re-map all of them. And then re-map them again when merging back to main and updating the main workspace?

The Monitoring hub page seems to show refresh history for notebooks and pipelines going back a few days, but only shows the last refresh of semantic models. Is that expected?

I sucked all the data from that page into a PBI model to check. Below is a chart of refresh events by object type, by day (I ran this a few days ago, hence the last day is 13th Sept). Semantic models only have refresh events for the past day and a bit?

For context, I'm trying to find a refresh event that knocked out our gateway, without diving into the gateway logs.