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Category: Microsoft Fabric

Partitioned Compute and Fabric Dataflow Performance

Published 2026-03-17 by Kevin Feasel

Chris Webb performs a test:

Partitioned Compute is a new feature in Fabric Dataflows that allows you to run certain operations inside a Dataflow query in parallel and therefore improve performance. While UI support is limited at the moment it can be used in any Dataflow by adding a single line of fairly simple M code and checking a box in the Options dialog. But as with a lot of performance optimisation features (and this is particularly true of Dataflows) it can sometimes result in worse performance rather than better performance – you need to know how and when to use it. And so, in order to understand when this feature should and shouldn’t be used, I decided to do some tests and share the results here.

Click through for the test, the result, and an open door for subsequent analysis.

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Dealing with Multiple Fabric Capacities

Published 2026-03-17 by Kevin Feasel

Jon Lunn provides some guidance:

You know you can have more that one capacity? Most of the clients I’ve interacted with, even since the Power BI capacity days, they have just purchased one big old capacity, and assigned it to every workspace they needed. There have been a few clients that have had multi-region capacities, spun up across the globe for thing likes, billing to specific cost centres and regions and data ownership and sovereignty issues, but for those that don’t have those issue, they just get a big capacity.

Jon provides some guidance on environment-based capacity planning. Even within an environment, there may be cases for carving out explicit capacity, such as data science activities that are occasional but potentially disruptive.

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XML Processing in Microsoft Fabric Realtime Intelligence

Published 2026-03-16 by Kevin Feasel

Reitse Eskens digs into some results:

I’ve been working for quite some time on a fun solution in Fabric Realtime Intelligence. We’re processing XML files into a structured table. As you’re probably aware, XML has its own… well, let’s be nice and call them challenges.

One thing I ran into was that an element contained several other elements. Usually, you’ll see them in an array, but in this case, it wasn’t. Since these elements within the main element contain the information we need for the table, I started thinking about how to extract this data.

Read on for an example of the type of Data Reitse was looking to process, as well as how the problem ended up being a lot easier to solve than first appearances would indicate.

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Ontology Rules in Fabric Activator

Published 2026-03-13 by Kevin Feasel

Ansley Yeo creates some rules:

Ontology Rules let you define conditions and actions on top of your business entities, rather than on raw tables or telemetry streams.

These rules are evaluated using Fabric Activator, which monitors and triggers actions when conditions are met. The unique value is that the rule logic is expressed in the language of your business, using ontology entities and properties.

Ontologies are a thing I’m not quite sold on yet, whether in Microsoft Fabric or elsewhere. I get the concept of what they do and the concept that this is business logic that the business side could theoretically do. What I have trouble with is seeing the practical benefits. Any time I see “Your business users can…” I immediately add in my mind, “But they won’t.” It feels like people getting giddy over object-oriented development over the data.

That said, I am actively learning about the topic, so maybe I’ll change my mind as I learn more.

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Microsoft Fabric Mirroring and SQL Server 2025

Published 2026-03-12 by Kevin Feasel

Meagan Longoria takes a peek at mirroring in Microsoft Fabric:

Mirroring of SQL Server databases in Microsoft Fabric was first released in public preview in March 2024. Mirrored databases promise near-real-time replication without the need to manage and orchestrate pipelines, copy jobs, or notebooks. John Sterrett blogged about them last year here. But since that initial release, the mechanism under the hood has evolved significantly.

Read on to see how this behaves for versions of SQL Server prior to 2025, and how it changes in 2025.

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Data Extraction from Unstructured Data with Fabric AI Functions

Published 2026-03-11 by Kevin Feasel

Sandeep Pawar demonstrates functionality:

Most enterprise data lives in free text – tickets, contracts, feedback, clinical notes, and more. It holds critical information but doesn’t fit into the structured tables that pipelines expect. Traditionally, extracting structure meant rule-based parsers that break with every format to change, or custom NLP models that take weeks to build. LLMs opened new possibilities, but on their own they bring inconsistent outputs, no type of enforcement, and results that vary between runs. What production workflows need is LLM intelligence with structured-output guarantees, delivered inside the data platform teams already use.

Microsoft Fabric AI Functions deliver exactly that. Functions like ai.summarize, ai.classify, ai.translate, and ai.extract let you transform and enrich unstructured data at scale with a single line of code – no model deployment or ML infrastructure needed. For the full list, see Transform and enrich data with AI functions.

Click through for an example. The tricky part of this is, because answers won’t be deterministic, you have to do a lot of testing and verification to ensure things are working reasonably well.

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Eventstream Not Sending Data to KQL Database after Resuming Fabric Capacity

Published 2026-03-11 by Kevin Feasel

Olivier Van Steenlandt troubleshoots an issue:

To continue the development of my mobile app, whose core ability is to scan barcodes of consumable articles and send them over for analytics, I’m resuming my capacity, starting to scan barcodes again, sending them to my Eventstream, and finally saving them in my KQL database.

After a couple of minutes, I wanted to validate all the scanned results in my KQL database and navigate to my scanned_barcode table.

Read on to see how Olivier diagnosed and corrected the problem.

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Logging and Error Handling with MicrosoftFabricMgmt

Published 2026-03-10 by Kevin Feasel

Rob Sewell continues a series on the MicrosoftFabricMgmt Powershell module. First up is structured logging:

If you have ever come back to a script the next morning and thought “what on earth happened last night?”, you understand why logging matters. Write-Host and Write-Verbose are fine for interactive use, but in automation — scheduled tasks, CI/CD pipelines, long-running jobs — you need something more structured. Something you can query, filter, and persist across sessions, something that you can provide to your team or support or auditors.

MicrosoftFabricMgmt uses PSFramework for all its internal logging, and that capability is available directly to you.

Then comes retry logic and dealing with long-running operations:

REST APIs fail. Networks are unreliable. Cloud services have rate limits. If your automation script does not account for this, it will eventually break at the worst possible moment. This is not pessimism — it is production experience.

MicrosoftFabricMgmt has a lot of error handling built in, so you do not have to write it all yourself. Today I want to show you what the module handles automatically, and how to add your own handling on top for the scenarios you care about.

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Deploy Microsoft Fabric Items with fabric-cicd in Azure DevOps

Published 2026-03-10 by Kevin Feasel

Kevin Chant announces a new Azure DevOps extension:

This post covers how you can simplify Microsoft Fabric deployments with “Deploy Microsoft Fabric items with fabric-cicd”. Which is an Azure DevOps extension that I recently published.

To manage expectations, this post shows how to start working with the extension and its associated task within the GUI-based classic release pipelines in Azure DevOps. Like in the below screenshot.

Read on to see how the extension works.

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Preview-Only Steps in Microsoft Fabric Dataflows

Published 2026-03-09 by Kevin Feasel

Chris Webb covers a new feature:

I have been spending a lot of time recently investigating the new performance-related features that have rolled out in Fabric Dataflows over the last few months, so expect a lot of blog posts on this subject in the near future. Probably my favourite of these features is Preview-Only steps: they make such a big difference to my quality of life as a Dataflows developer.

The basic idea (which you can read about in the very detailed docs here) is that you can add steps to a query inside a Dataflow that are only executed when you are editing the query and looking at data in the preview pane; when the Dataflow is refreshed these steps are ignored. This means you can do things like add filters, remove columns or summarise data while you’re editing the Dataflow in order to make the performance of the editor faster or debug data problems. It’s all very straightforward and works well.

First up, that feature is pretty interesting, though I could see things break if you only do your testing in the preview pane. Second, what Chris does with this is quite interesting.

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