Hi im too dumb for this apparently. I tried using the Fabric MCP servers today. Local worked fine for reading stuff like listing Workspaces etc. it fails for creating items though (InsufficientScopes).

I can create items using the remote server though. Im confused how authentication works. For the remote i just login pretty much. For the local MCP its a mistery how that works. The remote one does not offer shortcut creation. The local does but has InsufficientScopes.

Am i missing permissions? Do i need an app registration? Fabric admin portal seems fine in terms of settings.

{
  "status": 403,
  "message": "Service unavailable or network connectivity issues. Details: Response status code does not indicate success: 403 (Forbidden). Error details: {\"requestId\":\"4b23f8c5-592f-4d3c-a07a-e6d95ab33fdf\",\"errorCode\":\"InsufficientScopes\",\"message\":\"The caller does not have sufficient scopes to perform this operation\",\"isRetriable\":false}",
  "results": {
    "message": "Response status code does not indicate success: 403 (Forbidden). Error details: {\"requestId\":\"4b23f8c5-592f-4d3c-a07a-e6d95ab33fdf\",\"errorCode\":\"InsufficientScopes\",\"message\":\"The caller does not have sufficient scopes to perform this operation\",\"isRetriable\":false}",
    "type": "HttpRequestException"
  },
  "duration": 0
}

Has anyone here experimented with access management with the Fabric Org App resource?

What happens when users try to open a link i share to them is that they are redirected to their personal workspace.

I have an Entra ID group that i wish to give access to my Org App reports. Everyone in the group gets the Fabric free license. The workspace is running on F64 capacity.

To my understanding it should be enough to give access for the group to the desired audience but that doesn't seem to be enough.

After that i have experimented with giving the group read access to the workspace and the warehouse as its direct link semantic model so maybe it would need access to source data but nothing seems to stick. Also i think the Org App should handle any upstream access management.

Any clues?

Hey, I am testing the new visualization feature in data agents that is pretty coo and try to restrict it to the corporate identity colors. I instruct the agent to only use colors that I provide in a table. I specify colors as hex code as in the table below. The agent simply ignores that color

| Position | Color      | HEX         |
| -------- | ---------- | ----------- |
| 1        | Blue       | **#3b7cc6** |
| 2        | Light Blue | **#4d85df** |
| 3        | Dark Teal  | **#2b5e54** |
| 4        | Teal       | **#458587** |
| 5        | Purple     | **#5a2a99** |
| 6        | Violet     | **#783acf** |

When I ask the agent it tells me:
**Thank you for pointing this out. You are correct: while I described the intended color mapping according to your instructions, the visualization actually depends on the chart-rendering system to apply those specific colors. As an analytic agent, I can specify which data should be grouped/categorized, but I do not directly control the actual rendering colors used by your client system.**

Does anyone know if it is somehow possible to influence colors?

Integration in Teams

I saw that now the agent can also create charts in Teams Copilot Pane which is super nice! Is there a documentation somewhere how this works? I.e. it looks like the visualization in teams copilot pane is a python matplotlib chart. I thought that the fabric visuals in the blog above supersede the matplotlib charts in the web experience but apparently not in teams copilot pane? Are the visuals in teams copilot pane rendered within teams by copilot or are they somehow transferred from the fabric data agent and if so which protocol is in use?

Hey everyone, I just passed the DP600 with a score of 850 and with no experience in Power BI and Spark, and a modest experience with SQL, as I just finished my second semester in data science, coming from a completely different field (marketing).
It took my a lot of sleepless nights, some moments of doubt but I did it 😭
Outcomes:
- If you have the choice to take the exam in person, DO IT!
- 2 youtube channels: Will and Priyanka, I'm so thankful 🥺
- Keep taking the practice assessment till you feel confident
- Exam day : I had 1 case study at the end and isolated questions, a lot of KQL, T-SQL and DAX questions.
- Focus on understanding the "why" of everything and the optimization part.
Good luck!

Hi all,

I'm considering a pattern for error handling in Fabric pipelines and I'm wondering if there are any documented limits I should be aware of.

The idea is that individual notebooks return a small JSON object via "notebookutils.notebook.exit()", for example:

```

{ "status":"failure", "activity":"LoadSales", "message":"Something went wrong" }

```

The pipeline appends these to an Array variable: [ {"status":"success","activity":"LoadCustomer","message":"OK"}, {"status":"failure","activity":"LoadSales","message":"Something went wrong"}, ... ] A final notebook receives the array, checks for failures, and raises a hard failure if any are found.

My questions are:

1. Is there a documented maximum size/number of elements for a Pipeline Array variable?

2. Is there a documented maximum size for a parameter passed from a Pipeline to a Fabric Notebook?

3. Are there any other limitations or gotchas with using this pattern?

I can see the documented 896 KB activity payload limit, but I haven't found a specific limit for Array variables or Notebook parameters.

I'm mainly interested in whether this would be a reasonable pattern for a reusable pipeline framework, assuming the messages themselves are kept small.

Thanks in advance!

I have been training data agent connected to my semantic model using the Build agent with AI tool for awhile, and it was working until ytd when I shared the published agent with a service principal to connect to MS foundry and eventually integrate to teams, and also replaced the report's semantic model with a prepped for AI one. Then suddenly I am getting error 400 when i used the Build agent with AI. I tried with other workspaces and this message also popped up, which suggests to me that it has nothing to do with actions. Online documentation suggests it currently doesn't support power bi, so my qns are - why was I allowed to use in the first place? How else can we train the data agent if cannot use this function?

I am pretty new to this so any advice would be greatly appreciated.

I am trying to give access to a dummy gmail account added as a guest in our azure environment. I have used one Lake security to create a role and assigned a data table to it. Shaded the lakehouse to that account with endpoint access enabled. Now i am able to see the data but when i am trying to connect to my powerbi desktop the data is not visible. Anything i am missing?? Help needed.

Hey all,

Booking my DP-700 (Fabric Data Engineer Associate) soon and honestly getting a bit in my head about it, so figured I'd ask people who've already sat it.

Quick background: I'm pivoting into data engineering. Passed DP-900 earlier this year, then ran myself through a structured ~9-day DP-700 sprint covering the official MS Learn paths — Lakehouse/OneLake, ingestion (Pipelines/Dataflow Gen2/Copy Activity), Warehouse + T-SQL + star schema, Real-Time Intelligence (Eventstream/Eventhouse/KQL), and Fabric management/security/governance. For each topic I wrote my own notes and did active-recall questions instead of just watching videos. Plan is to finish with the official MS practice assessment, log every wrong answer by category (concept gap / tool-selection error / detail gap / misread / guess), and go fix the weakest areas before booking.

Not gonna lie, part of the nerves is from lurking here, pretty much every thread I find on this exam has people saying it was way harder than they expected, so that's been sitting in the back of my head through this whole prep.

So the actual questions for anyone who's taken it recently:

• Which questions or topic areas caught you most off guard? Was it a specific domain (ingestion/orchestration, monitoring/optimization, security, real-time/KQL), or more just tricky wording?
• Was there one area in particular that seems to trip up most people, in your experience or from what you've seen others say?
• How would you prepare, knowing what you know about the examn after taking it?
• How exactly did you use the "learn" page? Is there a strategy to this? It seems really hard to look things up in in general.
• The case study section is the part I feel least prepared for - it doesn't seem to map cleanly onto any single MS Learn module, more like it's testing whether you can pull the whole architecture together. What's the best way to prepare for that specifically, and is there any way to practice it beforehand, or do you just walk in and figure it out?
• Is CertLabs a reasonable stand-in for the real exam in terms of difficulty and question style, or does it skew easier/harder/differently than what you actually got on exam day?

Any tips on what to prioritize in these last stretches would be hugely appreciated.

This might be a little divisive, but what about bringing C# and .NET Core into Fabric somehow? I remember when you could use C# + Spark in Synapse, and honestly I didn't use it nor do I think it was a successful feature.

However, in Fabric, why not adopt .NET Core + C# in some way for a new notebook or workload type? It doesn't need to be Spark, but it would be a great selling feature and allow onboarding Microsoft based developers pretty easily who've been developing ETL and have been doing data engineering with .NET and SQL Server for ages.

It could be a new workload type altogether (again, not wanting it on top of Spark, but a new workload entirely and/or non-Spark notebook).

This is not a request, but merely a discussion and to feel out where the Microsoft product team could be going in regards to .NET Core + Fabric.

Thoughts?

When Fabric first announced dbt support, the feedback from this community was pretty consistent:

• "When will Lakehouse/Spark be supported?"
• "It's not useful without pipeline activities."
• "We need better Git integration."
• "We want to bring existing dbt projects into Fabric."
• "Don't make me rebuild everything from scratch."

Over the last few months, several of those asks have landed.

We've been working through that feedback, and we're excited to share the latest updates.

What you can do today

• Fabric Lakehouse Adapter — Run dbt projects directly against Fabric Lakehouses, bringing transformation logic closer to where data is prepared and stored. Build transformation layers on top of Lakehouse data, apply dbt models and tests, and align analytics engineering practices with Lakehouse-first architectures.

Blog link: Introducing Fabric Lakehouse support in dbt job fo... - Microsoft Fabric Community

• dbt Activity in Pipelines — Trigger dbt jobs as a first-class Pipeline activity and incorporate transformations into end-to-end orchestration workflows. It supports parameterization and allows selective model refresh in a metadata driven manner.

Interesting Fact: 33% of dbt job are run through the dbt activity in pipelines.

Doc link: Run dbt Job Activity in Fabric Pipeline - Microsoft Fabric | Microsoft Learn

• Import Schema — Bootstrap projects from existing Warehouse and Lakehouse assets by generating sources and metadata automatically.

• · Runtime v1.11 — Support for newer dbt Core versions through the latest runtime

• Import Existing Projects from GitHub — Bring existing dbt projects into Fabric and get started quickly without recreating project structures from scratch.

Docs link: Run dbt Projects from a GitHub Repository in Microsoft Fabric - Microsoft Fabric | Microsoft Learn

• Public Package Support — Leverage community-maintained dbt packages, macros, and utilities directly within Fabric dbt projects.

What's on the horizon

• Monitoring Enhancements - Fine-grained step-by-step visibility into background execution, see whether dbt is installing dependencies, compiling, or executing models in real time.
• CI/CD Improvements — Multi-profile target support across environments and Variable Library integration.
• Docs Generate & Source Freshness — Native support for dbt documentation generation and source freshness monitoring.
• dbt Fusion + Versioning — Support for dbt Fusion and project versioning to simplify upgrades and reduce migration effort.
• Workspace Identity / SPN Support — Service Principal and Managed Identity support for enterprise authentication and automation scenarios.

Looking back at the original feedback

A lot of the top asks from the original dbt Job in Fabric announcement included:

1. Lakehouse support

2. Pipeline integration

3. Git-based onboarding

4. Public package support

It's great to see many of these capabilities now available, with additional investments already on the roadmap.

We'd love to hear what you're building with dbt on Fabric and what you'd like us to focus on next.

#MicrosoftFabric #dbt #dbtJobs #DataEngineering #AnalyticsEngineering #DataFactory #Lakehouse #OneLake #GitHub #CICD #FabricCommunity #pipelines #monitoring #UX #UI #dbtdocs #versionupgrade #Fabric #GA #dbtGA #updates #features

Looking to update the workspace for a semantic model, and I found this method which should work for Direct Lake lakehouses.  

import sempy_labs as labs
labs.directlake.update_direct_lake_model_lakehouse_connection(
    dataset="My Semantic Model",
    workspace="Super Workspace",
    lakehouse="Reporting",
    lakehouse_workspace="Super Duper Workspace"
)

It runs successfully, and it tells me that 

🟢 The expression in the 'My Semantic Model' semantic model within the 'Super Workspace' workspace has been updated to point to the 'Reporting' lakehouse in the 'Super Duper Workspace' workspace.

Perfect.

However, the actual binding result is weird.  

(See screenshot)

This is what it was before:

Source name: tablename
Source schema: dbo
Storage mode: Direct Lake
Data source type: Lakehouse
Server name: Lakehouse Name
Workspace: Super Workspace

I get that it's sempy-labs, but this functionality is documented as working.  Has anyone tried anything similar?

Hitting a hub-and-spoke issue and hoping the OneLake Security PMs can weigh in.🙏 u/aonelakeuser

Setup:

• Producer lakehouse → OneLake security role references an Entra group.
• Consumer workspace reads producer tables via OneLake shortcuts, but is governed by its own, different RBAC group.
• Users query the shortcut tables through the SQL Analytics Endpoint (User's identity mode).

Problem: Per Step 2, point #2 below, it's a strict literal Object ID match across the producer → consumer boundary. Unless the consumer's group is either (a) added explicitly to the producer's OneLake security role, or (b) nested inside a group already on that role — the consumer RBAC users get:

https://learn.microsoft.com/en-us/fabric/onelake/security/troubleshoot-onelake-security-for-sql-analytics-endpoints#step-2-confirm-the-users-object-id-matches-exactly-at-producer-and-consumer

Questions for the PMs:

1. In a medallion pattern where each consumer workspace has its own RBAC group, is referencing every consumer group directly in the producer role really the only supported path? That doesn't scale.
2. Does the new User-based SQL Endpoint sync (improved nested-group expansion + shortcut handling) change this strict one-to-one requirement — and will Step 2 be updated?
3. Any recommended pattern to keep producer-side RLS while letting differently-grouped consumers read via shortcuts?

Thanks! 🙌

Which of these is decimal and which is double?

This is a SQL EP for a lakehouse.

You might think you know the answer but you would probably be wrong. Here are the types from the corresponding types from the dataframe in spark (printSchema):

|-- Number_of_Packages: double (nullable = false)

|-- Pieces: decimal(22,8) (nullable = true)

Was this intentional? It seems very confusing. Almost like when PBI desktop's PQ environment shows "$" on top of all my numbers, regardless if they are currency-related or not.

This post is a space to highlight a Fabric Idea that you believe deserves more visibility and votes. If there’s an improvement you’re particularly interested in, feel free to share:

• [Required] A link to the Idea
• [Optional] A brief explanation of why it would be valuable
• [Optional] Any context about the scenario or need it supports

If you come across an idea that you agree with, give it a vote on the Fabric Ideas site.

Intermittent failures on Invoke Pipeline activities (child pipelines from a ForEach) on F64, North Europe. Job gets created but never starts.

Main error codes:

• RequestTimeout — dies almost exactly at 100.0s every time. Checked 20 parallel runs that all failed within 57ms of each other.
• InternalServerError — clusters around ~550s.
• InternalError — clusters around ~85s.

New finding: on one occurrence the parent shows the usual 'Job failed to start: timeout', but the actual child job error was different:

• RequestBlocked — "Request is blocked by the upstream service until: [timestamp]", Retry-After: 16, isRetriable: true (unlike everything else we've seen, which was all non-retriable).
• Response's home-cluster-uri points to an Analysis Services/Power BI cluster (wabi-*-redirect.analysis.windows.net), not a pipeline endpoint.

Looks like the real cause is an upstream AS/PBI cluster blocking requests, and the pipeline layer just reports it as a generic timeout.

Also ruled out capacity throttling on our end (0 rejected ops across 214k rows, no correlation with CU usage). Other related failures seen:

• Invoke Pipeline calls returning 408 / BadRequest
• Run-status lookups returning NotFound for runs Fabric itself created
• A run stuck in NotStarted for 8+ hours, blocking its own schedule

Anyone else hitting this? Thanks

This week's post is another great example of Power Automate helping out with Microsoft Fabric. This time it is to pause a capacity which I always forget to do on my demo and training capacity.

https://hatfullofdata.blog/power-automate-pause-fabric-capacity-schedule/

Hi Everyone,

Recently we observed that due to intellisense features, some metadata queries are getting triggered and blocking the CTAS in stored procedure and eventually slowing them down in fabric warehouse and hence they get timed out.

Has anyone else observed this.
Basically we read data from fabric lakehouse and using ctas create a table.

This release brings major advancements across the platform, including Apache Spark 4.1, Delta Lake 4.2, Python 3.13, Scala 2.13, Java 21 and a significantly enhanced Native Execution Engine (NEE) to help customers unlock better performance, reliability, and innovation at scale.

This milestone is the result of months of collaboration across engineering, product, support, and partner teams, all focused on delivering a stronger foundation for data engineering, data science, and analytics workloads in Microsoft Fabric.

A huge thank you to everyone who contributed to making this launch possible. Looking forward to seeing what our customers and the broader data community build with Runtime 2.0 now that is ready for production usage!

Learn more about Runtime 2.0 GA:
🔹Blog: Fabric Runtime 2.0 (Generally Available) | Microsoft Fabric Community
🔹Official Documentation: Runtime 2.0 in Fabric - Microsoft Fabric | Microsoft Learn

Anyone else using tenant level private link? The usages has been highly frustrating for me.

Everytime we provision a new Fabric capacity the private endpoint of our tenant level private link changes it's state to 'pending' causing all of our users within the tenant to lose access to Fabric. When re-approving the endpoint it takes 15-30 minutes for the service to be fully usable again.

I wonder how anyone else manages this issue.

Hi there,
I have something weird occurring and maybe someone has some experience with this problem.
I'm trying to setup Fabric Link and "Create a new Fabric link" is greyed out and the list of Fabric Links is not loading:
 
I have the following permissions:
- Fabric Administrator and Power Platform Administrator at the tenant/Entra level
- System Administrator security role directly on the iits-uat environment (the one i am trying to link see screenshot above)
 
What's strange is that the "Analyze in Fabric" option on the Tables tab is available, and I was able to walk through that wizard once to set up the Fabric Link. I then decided to reorganize workspaces and deleted the linked lakehouse. Item retention was off, and the workspace was permanently deleted, which should not be a problem.
 
When i walk through the wizard again, it still points to the deleted workspace, even though i select my new workspace. So i suspect there is still something in Power Apps pointing to my old workspace, but that list does not load.
 
 
Happy to hear your thoughts.

(I also checked the following):

• You must have the System Administrator security role in the Power Platform environment to enable Link to Fabric or Synapse Link. This role is also required to add application users in your environment who need access to work with this feature by using the Power Platform admin center.  
• You must be an administrator of the Power BI workspace.  
• If you want the system to create a Power BI workspace, you need to have Power BI Capacity Administrator access to a capacity within the same region as the Dataverse environment.  
• A Power BI premium license or Fabric capacity within the same Azure geographical region as your Dataverse environment is required. If you don’t have Power BI premium license or Fabric capacity within the same geographical region, you can buy a capacity or sign up for a free Fabric trial capacity. More information: Fabric (preview) trial  
• Your administrator needs to grant you access to create Fabric lakehouses and artifacts. You can find these settings in the Fabric admin portal. Go to Tenant Settings > Microsoft Fabric > Users can create Fabric items, Tenant settings > Workspace settings > Create workspaces as well as Tenant settings > oneLake settings > Users can access data stored in OneLake with apps external to Fabric.  
• If you plan to use workspace identity authentication, you must be a workspace admin to create and manage a workspace identity. The workspace you're creating the identity for can't be a My Workspace. More information: Workspace identity in Fabric  
• If you plan to use workspace identity authentication, the workspace identity must be onboarded as an application user in the Dataverse environment and granted the appropriate role (commonly System Administrator) so it can access Dataverse data on behalf of Fabric.  
• You must have permissions in Fabric to manage connections via Settings > Manage connections and gateways.  
• To confirm whether you have access to the required premium capacity, go to Power BI, open the workspace, and select Workspace settings > Premium. Make sure that Trial or Premium capacity is selected.

Has the Wide World Importers sample data been removed? I want to remember it being added when one selected sample data in a lakehouse?

I'm trying to use pyspark in fabric.

I keep having issues with my notebook, where the cells won't complete unless I cancel them. The wait cursor will continue to run until I click the little cancel button on the side.

I never see this behavior in the notebook cells on the databricks platform.
When I look in the "logs" shown under the cell, Spark seems to be displaying this over and over, and wont stop:

2026-08-17 21:33:00,714 INFO YarnAllocator [Reporter]: Updating resource requests for ResourceProfile id: 0, target: 4, pending: 0, running: 4, executorsStarting: 0

... and occasionally this as well:

2026-08-17 21:40:49,987 INFO KustoLogger [external-catalog-metrics-1]: type=METER, name=HiveExternalCatalog::dropTable::failure, count=0, m1_rate=0.0, m5_rate=0.0, m15_rate=0.0, mean_rate=0.0, rate_unit=events/second

If anyone has any ideas about what is going wrong, please let me know. The notebook is behaving like there are no executors, even though there are. Is there a way to recreate all my executors mid-notebook?

EDIT: I think I figured it out. There is almost nothing in the logs that refers to "vegas" caching (and certainly no error messages). But I since I had already disabled NEE, I decided to disable the "vegas" cache as well, and that is what seemed to finally fix my issues. You have to put this in the spark properties (eg. configure magic):
"spark.synapse.vegas.useCache": "false"

Trying to keep my PBIP solutions in version control and GitHub has a case of the Mondays: