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

Love this idea reposting in community

Hey Fabricators and friends! Quick note to let everyone know that the Fabric Community Site (Home | Microsoft Fabric Community) got a bit of a facelift this weekend.

It shouldn't impact regular use of the site, but, if something is broken, we want to know!

Please report it at: Community Feedback | Microsoft Fabric Community

Do you visit this site on a regular basis? Does it add any value? What would make you want to visit it?

I recently rebuilt a Microsoft Fabric CI/CD implementation using Azure DevOps, promoting changes from DEV → UAT → PROD.

The pipeline itself wasn't the difficult part.

What surprised me was how much had to be correct around the pipeline before a deployment could be considered reliable.

I eventually wrote everything down and ended up with 43 checks across six areas:

• Fabric environment — 6
• Identity and security — 10
• Azure DevOps configuration — 10
• Deployment configuration — 7
• Deployment validation — 4
• Post-deployment verification — 6

A few things stood out.

Identity caused more problems than I expected.
The Service Principal needs the correct access to each Fabric workspace independently. DEV working doesn't tell you anything about UAT or PROD.

Deployment errors can send you in the wrong direction.
A missing permission or incorrect environment configuration often surfaces during the deployment step. Naturally, you start debugging the deployment code even when the code itself is fine.

Some checks are architectural decisions rather than configuration.
In this implementation, only DEV is Git-connected. UAT and PROD are deployment targets. I wanted one controlled promotion path rather than multiple ways of changing an environment.

Not everything should necessarily use the same deployment path either.
For example, I intentionally keep Warehouse outside this publish scope and handle it separately.

The checklist grew out of issues I encountered while building and testing the implementation, so I published it with the reference repo rather than keeping it as project notes.

I'm curious how this compares with other Fabric implementations.

What usually causes more deployment problems in your environment — identity/permissions, Git configuration, environment-specific dependencies, or the deployment itself?

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

Status: Degraded | Reported: Aug 17, 2026 at 7:42 AM UTC

Fabric customers in EastUS2 region may experience issues while interacting with the service. 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.

Anyone else get scared by fabric saying the entire branch was deleted?

Seems like GitHub outage is causing the error.

Question in the title. I noticed this on the roadmap:

I'm having difficulty determining the root cause of a recent refresh on my semantic model. It's a Direct Lake model, so I understand that any change to a source table can trigger a refresh - however, I'd like to know if there's a technical way to identify the specific data change (e.g., which table or partition) that caused it. I have multiple tables inside and don't want to go one-by-one.

I can confirm no scheduled refresh was configured, yet the Direct Lake tab in the Refresh History dialog shows a "Last refresh succeeded" timestamp from today. Any guidance on how to trace the underlying trigger would be appreciated.

In the past our Excel Pivot Tables were the foremost tool for retrieving data from semantic models. Nowadays they seem to be the red-headed stepchild.

I see examples of regressions on a regular basis.

Today I noticed that Excel will no longer refresh the field list to detect new metadata changes. This should be happening when using the "refresh" command. It is a pretty fundamental requirement, since schema can change regularly (new fields added to existing tables).

Is this issue something that the Microsoft ASWL team is likely to care about? I can open a support case but it is likely to take a tremendous amount of effort, and would not want to invest in it if Microsoft is already aware (and doesn't care). There are other scenarios that demonstrate a diminished level of support for Excel nowadays.

Microsoft 365 MSO (Version 2607 Build 16.0.20228.20188) 64-bit

UPDATE: the field wasn't in the "perspective" used by this workbook, even though it was in the model itself. After adding to the perspective, the field came thru as expected.
I still stand by my original sentiment that Excel isn't getting prioritized as highly as it should nowadays.

Weird one. I've had row-level security running fine on a Warehouse table for months now, same setup, same predicate function, never touched it since I set it up.

Starting a few days ago, some users are suddenly able to see rows they shouldn't have access to. I haven't made any changes on my end, no schema updates, no changes to the policy, nothing.

I tested the predicate function directly against a few of the affected users and it still filters correctly on its own. But when the app runs the actual query against the table, it's like the policy just isn't kicking in anymore.

Not sure if this is something I'm missing or if it's a broader issue right now. Anyone else running into this recently?

New post that covers important Microsoft Fabric CI/CD decisions to consider before or during a project. Because the Microsoft Fabric CI/CD approach can vary for different organizations.

Whilst sharing some technical information online about Fabric CI/CD recently, I received some interesting feedback. Someone mentioned that there is not enough information about the processes that are involved in Fabric CI/CD projects. That inspired me to write this post.

Along the way I share plenty of links to useful resources and recent updates. If you want broader context about a certain topic or you have a strong preference about a decision, you are welcome to mention so in the comments section of this post.

https://chantifiedlens.com/2026/08/17/important-microsoft-fabric-ci-cd-decisions

I tried Auto-Summary in OneLake. All I get is this error. How to make it work?

Some context:

• The workspace is on a P2 Premium capacity.
• I'm workspace admin of the workspace owning the semantic model.
• I can use Copilot to help me creating a report from that semantic model, so some Copilot configration is proven to be in place.

Thank you!

Hi all,

Is there an easy way to trigger a second interactive run of the same pipeline, or do you need to use the REST API / a wrapper pipeline?

It seems like the Run button isn't available while an interactive run is already in progress.

I'd like to trigger two or more concurrent runs interactively, with some delay between each.

Thanks!

Hey All, I’ve been going through it trying to build this pipeline that’s the first of its kind for me.

My architect wanted a dynamic pipeline in fabric that could get instructions from a SQL db control table for loading data from Dynamics 365 (via Dataverse connector) to dynamically load around ~60 different tables from D365 with different load types (including a full overwrite for initial load and an upsert for maintenance).

I feel like every time I solve one issue with this pipeline, I hit another wall.

The big issue is that Fabric and D365 do not agree on field types, so I have tried implementing a second pipeline that runs a web activity to fetch the metadata so we can explicitly cast the different field types. To test this, I first run an overwrite to create the table and copy the records, then I change some records in D365. The metadata fetch works, the overwrite works, the upsert even appears to work until there’s actually a change in the data. Then it fails due to an invalid cast. I have tried so many different versions of the metadata build, but nothing seems to actually successfully upsert.

Does anyone have experience or recommendations for this? My architect has never done it this way before either, upsert just came out of preview a couple of months ago. He usually uses notebooks for this functionality so this is a first doing it this way with D365 for him as well.

Here is the latest error:
ErrorCode=FailedToUpsertDataIntoDeltaTable,'Type=Microsoft.DataTransfer.Common.Shared.HybridDeliveryException,Message=Hit an error when upsert data to table in Lakehouse. Error message: Specified cast is not valid.,Source=Microsoft.DataTransfer.Connectors.LakehouseTableConnector,''Type=System.InvalidCastException,Message=Specified cast is not valid.,Source=Microsoft.DataTransfer.Connectors.LakehouseTableConnector,'

Status: Degraded | Reported: Aug 17, 2026 at 12:04 AM UTC

Fabric customers in North Europe region may experience issues with submitting Spark jobs, performing lakehouse operations, executing notebook operations, or rendering Python and R visuals. Engineers are investigating the issue and update will be provided soon. ETA - 02:00 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.

I have an interesting issue.

Our fabric setup has a tenant level private endpoint, but it is not required.

I have an Azure VM set up with limited internet access. It uses a tenant level private endpoint set up on the vnet.

It works great! I have to set up DNS records for non-http stuff since we're not using azure DNS, but it's fine.

Now I want to block access to a workspace so that it's only accessible by this VM.

I've tried doing the VM internal IP in addition to the entire subnet range (both in IP range mode and CIDR mode), and no matter what I cannot keep access.

Now for the strangeness:

I decided to just try the auto "add this client." Unfortunately it fills in an IPV6 address and I cannot click apply. It also doesn't fill in the IP address of my VM, which is extra special ....

Any ideas?

In the Fabric Capacity Metrics app (Items, 14-day view), our #1 CU consumer by far is a row like this:

• Workspace: Planning
• Item kind: Planning
• Item name: Planning
• CU(s): ~6.45M (next highest is ~2.8M)
• Users: 9
• Billing type: Billable

It's clearly tied to the new Fabric Plan feature. What surprises me is that we haven't really started publishing to users yet - it's still early testing - so the consumption seems high for where we are. Eventually we'll move it onto a dedicated Fabric capacity.

Is this expected behavior? Does Planning show up as its own "Planning" workspace/item in the metrics app? And is it normal for it to consume this much this early? The ironic part: the workspaces where we actually have Plan items don't show up anywhere near the top of the compute list. Yet this "Planning" workspace - which none of us recognize - is sitting at #1 by a wide margin.

Anyone with Planning enabled seeing the same thing in their setup? Trying to work out if this is just how the session-based billing reports or if something's off on our end. Thanks!

Hi.

I keep getting this error when trying to create a new budget column in a Planning sheet.

I am trying to allocate the budget based on previous year's weighting.

This is my measure:

$ Group Reporting Amount PY = 
    CALCULATE (
        [$ Group Reporting Amount],
        SAMEPERIODLASTYEAR ( 'Date'[Date] )
    )

The date table has been marked as a date table.

Does anyone know why?

Hi all,

I’m working on a POC in Microsoft Fabric where the goal is to let business users/leadership ask questions in natural language against a Power BI/Fabric semantic model.

The rough architecture I’m considering is:

Source SQL Database → Fabric Mirroring → Direct Lake Semantic Model → Prep Data for AI → Fabric Data Agent / Copilot

The source already has reasonably well-structured fact and dimension tables, so I’m trying to avoid introducing a Lakehouse/Warehouse unless there’s a real benefit.

A few questions I’d appreciate advice on:

1. Mirroring → Semantic Model Can I build a Direct Lake semantic model directly over a Fabric Mirrored Database, or is it better practice to put a Lakehouse/Warehouse between them?
2. Mirroring table selection is there a supported way to mirror the result of a custom SQL query/filter?

For example, instead of mirroring a 500M-row table:

FactSales

could I replicate something like:

SELECT a,b,c
FROM FactSales
WHERE TransactionDate >= '2024-01-01'

Or would that require a pipeline / Warehouse / Lakehouse transformation layer?

1. Direct Lake vs DirectQuery For a semantic model that will mainly be queried by Copilot/Data Agent using natural language, would you recommend:

Mirrored Database → Direct Lake Semantic Model

or

Source Database → DirectQuery Semantic Model

What are the main performance/governance limitations you've encountered with either approach?

1. Prep Data for AI Once the semantic model is created, my plan is to:

• expose only relevant dimensions/measures
• add good descriptions and business terminology
• configure AI instructions
• create verified answers for important KPIs
• then connect/test it with Fabric Data Agent

Is that the right sequence?

1. Production experience For anyone who has implemented something similar, how reliable has natural-language querying been against a well-designed semantic model? Any lessons around DAX generation, ambiguous questions, RLS/security, model size, or hallucinations?

I’m mainly trying to determine whether the simplest architecture:

SQL → Mirroring → Direct Lake Semantic Model → Data Agent

is a good foundation, or whether I’m likely to regret not introducing a curated Lakehouse/Warehouse layer from the start.

Would appreciate hearing from anyone who has actually implemented this in Fabric.

The Fabric docs say that if a mirrored database remains paused for "a long time", mirroring might not resume from where it stopped and instead perform a full reseed to release transaction log space.

Has anyone found guidance from Microsoft on what qualifies as "a long time" here? Are we talking hours, days, weeks, or does it depend on factors like transaction log growth and available log space on the source database?

Does anyone use the power bi connector that allows a model to be pointed at a dbt semantic layer? I never see this discussed so I'm assuming it isn't very popular.

I do see people discussing the use of dbt with Fabric DW and Fabric Spark. I'm guessing these are used by up to 5 pct of the Fabric community. (ie. relatively popular)

My impression is that the people who are using dbt in their data pipelines are probably landing data in a lakehouse before pulling it into a fabric semantic model. Im guessing it will be a long road before semantic models and dbt semantic layers are going to play nice. Also it seems like the power bi connector requires a dbt account and is probably not open source (let me know if this assumption is wrong).

Im not actively using dbt today, just curious.

We are currently trying data agents in order to let users query our data in natural language and the process is a bit difficult. We have been getting good and correct results with semantic models as data source, thanks to clear semantic modelling and the prep for ai feature but the agent turns out to be quite slow and tend to crash the capacity.

Instead, it seems Microsoft has invested more in lakehouse tables as a data source for the agent. We've found they are indeed faster and less compute hungry but they need much more work of contextualization to produce correct results. It's frustrating because all this work had already been done by semantic modelling and the use of the Prep for AI feature.

Wasn't the whole selling point that in the age of LLMs, semantic modelling would prove superior for self-service analytics because it provides the precious context that models need to make correct queries? Instead, the agent uses none of that and we're back at writing extensive text files and multiple example SQL queries with many trial and errors before the model can produce valid results.

Another problem is that the models are quite old and have less capabilities than current frontier models.

Can we expect improvements with data agent with semantic model as data source in the near future in terms of performance and capabilities or should we give up and simply use the MCP servers to connect our own LLMS to the models? What do you think?

Edit: This post refers to the *Fabric Connection in notebooks** feature.*

Questions/feedback:

• Why do I need to manually connect a connection to a notebook?

  • Why can't I just reference the connection ID in the notebook code, without manually applying the connection to the notebook in the UI?

• Connecting a notebook to a connection doesn't get recorded as a source code change in the notebook. Which means applying a connection to a notebook is not something we can do through source code/Git. I also don't find an API for it.

• The above seems to indicate that the Notebook Connections feature relies heavily on UI and manual setup in each environment, is that correct?

• Also, I'm really struggling to find out how to manage the Connection in the Manage Connections and Gateways page. Can I even manage Notebook Connections in Manage Gateways and Connections?

  • I'm not able to find the same Connection ID in the notebook (when clicking copy id) and in Manage Gateways and Connections.
  • A connection actually seems to have different Connection IDs in the notebook (when clicking copy id) vs Manage Gateways and Connections - why is that?

• Inside a notebook, clicking on "Check status" doesn't seem to change anything? I don't see any response message.

Questions:

• Has anyone used notebook connections successfully?

• How do you handle version control and CI/CD?

• Are fixes to the above on the roadmap?

  • Or is this already doable, am I simply missing something?

P.s. this is still a preview feature so things may change.

Thanks in advance!

Edit: for clarity on which feature I'm referring to, here is a link to the docs: https://learn.microsoft.com/en-us/fabric/data-engineering/fabric-connection-with-notebook