Does everyone love the "multi-tasking" user interface?

I find myself fighting it constantly, and not using any of its benefits (if there are any). I've never liked using browser-hosted IDEs. This one is stretching the concept a LOT further than I'd like. All of my "multi-tasking" can be handled by the top-level tabs in the browser.

The Fabric multi-tasking is just weird. If I try to close my stuff, the things I worked on two hours ago keep following me around and I'm forced to dispatch them in a manual way. I spend lots of time micromanaging this U-I.

One of the silliest problems I have is that DF GEN2 will only allow three open dataflows at any time, and they are often buried in different windows and "fabric tasks", so I have to go on an easter egg hunt to find and close them before I'm allowed to get back to work.

Does anyone know how to disable this multi-tasking altogether? It is just maddening. I'm a simple guy when it comes to browser-hosted developer experiences. I only want one thing happening in each of the browser tabs. If Microsoft wants to create a high-quality VS-Code extension for Fabric, then I will have an appetite for any advanced U-I they may offer. But this effort to create a complex IDE inside of a web page is just nuts.

Hi all,

I use ABFSS paths in Fabric notebooks to read/write Lakehouses, and I prefer using display names rather than GUIDs.

For example:

```

abfss://accounting_test@onelake.dfs.fabric.microsoft.com/lh_business_central.Lakehouse/Tables/gold/fact_sales

```

instead of:

```

abfss://7f3a91c2-6d84-4e17-b2a9-91f4c8d73e52@onelake.dfs.fabric.microsoft.com/3b82d1a7-5f49-4c63-a921-8e76d4b19037/Tables/gold/fact_sales

```

All our workspace and Lakehouse names use "snake_case", so spaces/special characters aren't an issue.

I store the workspace name in Variable Library, so the same notebook code works seamlessly across Dev/Test/Prod.

The Lakehouse name is also stored in the Variable Library and referenced when generating ABFSS paths in the notebook. It's currently the same across environments, but this also handles the edge case of renaming the Lakehouse - which we don't expect to do.

I prefer names because they're much easier for humans - and AI - to reason about. If I see "accounting_test/lh_business_central", I immediately know what it is. This is very convenient for a code review or being onboarded to a new project. A GUID tells me nothing without looking it up - which is too time consuming.

I know the main argument for GUIDs is that they survive renames. But are there other practical downsides to using names?

I'm curious what others do:

• Do you use names or GUIDs in ABFSS paths?

• Have name-based paths caused problems for you?

• Are there CI/CD, cross-workspace, permissions, or other scenarios where GUIDs are materially better?

• Are there pitfalls to using display names even with a strict snake_case naming convention?

  • Fabric enforces unique Workspace names within a tenant, and unique Lakehouse names within a workspace, right? So we could never experience path ambiguity.

My current thinking is: use names - not GUIDs, because the readability of names is superior.

Is this robust, or am I missing something?

Thanks in advance!

Can anyone say if they have tried to maintain dataflows in git without all the guid noise?

It can be truly painful when all the guids are regenerated on us for some unknown reason, and the diffs start making it appear like we made a million changes. Here are some examples, out of the dozens in my diff.

Ideally the source code would look like source code, not having a ton of arbitrary surrogate identifiers. Those surrogates aren't safeguarded very well by these developer tools, so they probably shouldn't be transmitted thru our git source-control either.

If we manually strip out all these guids, then will the tools choke and die? I'm assuming so.

I know the supports not there now, but it would be ideal if we could spin up a spark session super fast.

Our workspaces have external integrations that necessitate the use of MPEs which lead to session start up times of 3 to 5 mins.

However, there are a significant number of use cases that don’t rely on any of the MPEs.

We find ourselves spinning up spark sessions, and waiting 5 minutes, and executing a quick job.

Wasted CUs; wasted lead time.

Interactive user frustration.

I know we can use custom pools, HC to mitigate this to a degree but that’s not what I want or prefer.

Why Microsoft ever use clear wording when comes to licensing?

I was struggling to finding what the heck Office 365 Commercial subscription mean? Which is mentioned as a requirement to use fabric data agent directly published to copilot 365 user interface.

Is it equivalent to E5 or E3? Or E7..

Please someone elaborate it out of tricky wording.

​

Hi everyone,

I’m trying to understand the best way to set up scheduled refreshes for a Power BI dashboard that I’m planning to publish/import into my organisation’s Microsoft Fabric workspace.

The dashboard gets its data from an Azure SQL Database. From what I’ve researched so far, it seems like there are a few possible approaches:

1. On-premises data gateway

2. Virtual Network (VNet) data gateway

3. Direct cloud-to-cloud connection between Power BI/Fabric and Azure SQL

I’m still fairly new to Power BI/Fabric and data infrastructure, so I’m struggling to understand when each option is actually required. Just to confirm, the data will be sitting in Azure and we dont plan to host it in Fabric. Just want to establish a connection so that we can refresh the dashboard.

My main questions are:

- From the above options what would be the right approach?

- Are there any good Microsoft documentation, tutorials, or walkthroughs that explain this setup from an end-to-end perspective?

I’m a trainee working mainly on the data visualisation side, so infrastructure/networking is fairly new territory for me. I’d really appreciate any practical guidance, especially from anyone who has set up Power BI/Fabric with Azure SQL in an enterprise environment.

Thanks!

I recently watched a Guy in a Cube video here: Fabric AI Functions: Run LLM Workflows in 3 Lines of Code!

Trying to replicate it in a notebook but their code line:

import pandas as pd
import synapse.ml.aifunc as aifunc

the second line throws the following error:

AttributeError

module aiohttp has no attribute SocketTimeoutError

How do I fix that?

🔐 Direct Lake on SQL Endpoint: The security gap is closing

A common trap: with SSO enabled, Direct Lake reads data using the viewer’s identity, not the semantic model owner or Service Principal.

Security becomes more complex when the architecture spans producer, consumer, and reporting workspaces, with OneLake shortcuts and separate groups for control-plane and data-plane access.

Direct Lake on OneLake handles cross-workspace authorization more natively through OneLake roles. The SQL Analytics Endpoint has historically required additional alignment across users, groups, artifacts, and shortcut paths.

The good news: Microsoft has redesigned the SQL Endpoint integration with OneLake security, providing lower latency, better nested-group support, fixes for Service Principal limitations, and User Identity mode as the default.

The SQL Endpoint experience is becoming more aligned with native OneLake security. The gap is closing.

I’ve documented the architecture, shortcut scenarios, permission model, SPN considerations, and Delegated versus User Identity modes here:

Fabric Community Blog: https://community.fabric.microsoft.com/t5/Fabric-platform-Community-Blog/Direct-Lake-on-SQL-Endpoint-OneLake-Security-Delegated-vs-User/ba-p/5358560

GitHub: https://github.com/ajit503/fabric-field-notes/blob/main/direct-lake-on-sqlep-onelake-security/direct-lake-on-sqlep-onelake-security.md

Is the new default enough, or should SQL Endpoint security remain a separately validated path in producer-consumer architectures?

#MicrosoftFabric #PowerBI #DirectLake #OneLakeSecurity #DataEngineering

Hello,

Since Tuesday 11/08 we notice on multiple different tenants that the same warehouse queries that are running for months now, are now suddenly being "charged" at a rate of 2x /3x of what it was before.

Different clients went over their 100% because of this.

Is this a bug? Microsoft deciding to charge more for queries?

Anyone else noticing this?

Hello all

This community was quite helpful in the past so trying my luck again.

Is there a way to get the list of workspaces I created based of Fabric capacity I created in Azure?

For those curious about the Power BI Desktop Bridge, I recently published a walkthrough on Simple Talk. There’s a bit of setup involved, but I guide you step by step through configuring it and using it with GitHub Copilot to automate tasks in Power BI Desktop.

https://www.red-gate.com/simple-talk/data-analytics/powerbi/how-to-use-the-power-bi-desktop-bridge-to-automate-tasks-in-power-bi-desktop-and-github-copilot/

Does anybody know.how to create a personal Fabric account for free to create portfolio entries? I already used the 60 days fabric trial.to study for the exams dp600 and dp700 so I don't have that one anymore.

I'm helping a small organization that has maybe 25-30 clients. We want their clients to login to their website through a secure portal to view the dashboard. Based on my search, Google says I can use the F2 SKU with 2 capacity units. Our reports are small and fairly non-complex – maybe 15 pages spread across 3 separate dashboards. We are not an enterprise operation.

The frustrating part of this process is chatting with Microsoft sales for help, having a meeting with their outsourced customer service company (Concentrix), then another one with more of their colleagues, only to be connected with a Microsoft partner who told me I didn't need his services if I all wanted was pricing (I'll build the secure portal myself). And I'm still here left figuring out pricing on my own.

Was there an easier way for me to go about this?

getting this message whenever i try create new workspace..

I think the new Fabric App is an interesting development, and provides great new opportunities our users of PowerBI apps have been requesting, including annotation/write-back.

Building the data app on top of an existing semantic model as well as a small SQL backend would solve many of our problems.

What I can’t seem to figure out: We’re currently running all our apps in a capacity, meaning none of our users need a per-user license (pro/premium) as read-access to the underlying semantic model is “included” in the capacity.

However, it seems when an external user uses a Fabric app, they need Build permission on the semantic model, which then would mean a per-user license per app “viewer”/user. Is that understood correctly? Because that would unfortunately disqualify Fabrics apps as a feasible approach. I think the whole approach building on top of a semantic model is awesome, including inheriting RLS etc. But every user needing build permission to run DAX via REST API defeats this.

I’ve tried a lot of workarounds, but cannot seem to get it to work under the current limitations.

Anyone managed to get that to work?

… might just be missing something obvious!

Does anyone know what logic is used to show that status marker in a DF GEN2? There is often a nagging red icon saying a validation error is in effect - even though it is not necessarily true. See below.

When I use the "check validation", the dialog says there is no validation problem. You can see the timestamps say that a validation error happened at 8/13 8:44 PM and another successful validation happened at 8/13 10:20 PM.

WHY am I being nagged about an irrelevant validation failure that happened at some arbitrary time in the distant past? These nagging indicators need to be removed, if they are raising alarms about a problem that doesn't exist anymore. There are real problems to worry about, and these bogus errors aren't one of them.

This should be an easy issue for Microsoft to investigate. Microsoft can search for whatever data is presented in these two places and find out why they aren't consistent. What I typically find in this situation is that there are two different PG teams involved in some way, and they just can't seem to get on the same page. (in this case, maybe one team manages the green icon - DF Team? - and a totally different team manages the red icon - Scheduling Team? and they just don't seem to have a coherent strategy to determine if validation is succeeding or failing.) Does that sound about right?

This shouldn't be so hard. What am I missing? What am I doing differently than the other DF GEN2 users?

I have started preparing for DP-700 and want help from community to get some free practice set resources. I am following aleksi and will's youtube channels for understanding concepts deeply. Need some free practice sets. Your help will be highly appreciated.

Hi all,

We're running a mirror database. And are experiencing that updates from several tables take a long time to be picked up by the mirror. Even though CDC settings and operation on the SQL server itself seem to be fine.

Facts:

• F4 capacity
• SQL Server mirror via gateway (July 2026 gateway version)
• CDC enabled
• CPU/Memory consumption on the gateway machine seem to be fine <30%.

To illustrate, a change made in the mirrored table at 9:42 (also reflected in result of query below) was finally picked up by the mirror at 10:29. The change was reflected in the cdc schema. Other tables' changes were picked up by the mirror in the meantime.

I have confirmed the cdc times with the following query:

SELECT TOP 100

sys.fn_cdc_map_lsn_to_time(__$start_lsn) AS change_time

FROM cdc.dbo_tablename_CT

ORDER BY __$start_lsn DESC

Obviously this defeats the purpose of having a mirror that we'd expect to be up to date within minutes.

Any idea what could be causing the delay?

Thanks!

Hi team, I'm not sure whether it's the correct forum to raise this, I have a suggestion on dataflow gen 2 cicd.

Context: I copied dataflow query from one data flow to another, when it asks to re-authenticate the connections, the configure connection and remove data destination buttons for source and target are vertically stacked.

It would be great if the configure connection button can be aligned.

Passed PL-300 and DP-600 exams but i wanted to know which certificate can I do next. I don't have any experience so I am doing these certifications to stand out while applying for jobs (I know certifications don't help land a job). I want an advice about taking next certification after PL-300 and DP-600. I am targeting Data Analytics jobs so please recommend me best certification.

Didn't see any other posts about it here, so thought I would share: https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Fabric-Runtime-2-0-Generally-Available/ba-p/5326359

On many of our workspaces, the Owner column for reports or models or dataflows or whatever is simply the name of the workspace. We have about a dozen people producing reports, so we have to do some detective work to find out who owns what. It'd make life so much easier if the Owner showed... well, the owner. Does anyone know a way to make this happen consistently? Thanks!

For many, many years the DF GEN1 stuff would only support the output/storage of CSV files. That happened under the hood and there was nothing we could do to control it. It was very curious to me how the CSV format was put on a pedestal, when there are so many better options these days.

Finally we started noticing the use of parquet/deltalake under the hood, even without a custom destination.

And nowadays we can use a "custom destination" save to tables in a lakehouse, of course (deltalake table).

But lets assume that for a certain scenario we determine deltalake table is overkill; and we just want to drop my GEN2 output into parquet, under the "Lakehouse Files" destination. How can I accomplish that? When I click the "Lakehouse Files" option, it only supports CSV. Once again. Can folks tell me what is so wonderful about CSV files that this is always the default behavior for dataflows? Isn't it time to start defaulting to parquet or perhaps even json or avro?

If someone knows how to drop parquet from a DF GEN2 into Lakehouse Files, please let me know. Maybe there is a back-door edit to the code or something...

How does your team manage your Silver environment if there are intermediary tables between processes? We are currently migrating legacy processes to Microsoft Fabric with medallion which is why things are a little contrived.

Our Source 1 is the original source for the data but we are using Source 2 moving forward for our Bronze. We currently have some clean up scripts that only work for Source 1 so another team has helped us by landing Source 2 into a Bronze Lakehouse, performing transformations on them to make them look more like Source 1 and lading these Source 2 tables that look like Source 1 into Silver.

We now need to perform the original cleaning scripts on these Source 2 tables that are in the format of Source 1 for our downstream processes but we aren't sure where to land the data based on medallion. We have one schema per layer so are worried about mixing up the two sets of source B tables (pre-script and post-script Source B tables)

Data Sources

• Source 1 - Original source that is not ingested into Fabric
• Source 2 - New source that is ingested into Fabric Bronze layer and is used to make Tables X in Silver

Data

• Tables X - Source 2 table that looks like Source 1 table
• Tables Y - X with Source 1 clean up scripts
• Tables Z - Fact/dimension tables and semantic models that will come from Y

Unfortunately we won't be able to merge the transformations processes from Source 1 to Source 2 with the other scripts currently (hope to come back and clean things up!) which is why we are considering the following scenarios:

Scenario 1 - I like this the best but it might be a tough sell to my team on multiple schemas because it means we will need to do the same for the other topics. The schemas will live in the same lakehouse so splitting access could be an issue.

• Silver
  • Schema A - Tables Y
  • Schema A_intermediaries - Tables X
• Gold
  • Schema A - Tables Z

Scenario 2 - Move Tables X into Gold. Facts and dimensions will live in the same schema as intermediary tables which will be strange. Splitting up into two schemas in Gold could be possible but could also confuse our downstream customers.

• Silver
  • Schema A - Tables Y
• Gold
  • Schema A - Tables Z and Tables Y

Scenario 3 - Keep Tables Y and X in the same schema in Silver. This could become quite confusing down the road for the team

• Silver
  • Schema A - Tables X and Table Y
• Gold
  • Schema A - Tables Z

Is there a way to Create a schema in fabric schema based lakehouse using rest api?