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Category: Microsoft Fabric

Building an Azure DevOps Extension for Microsoft Fabric

Published 2026-09-15 by Kevin Feasel

Kevin Chant gives us the rundown:

When I first released the extension, I decided to keep the source code private. Having invested significant time developing and testing it, I wanted to first establish the extension.

Now that the source code is available in a GitHub repository I decided to share my experiences to help others create their own Azure DevOps extensions. Because there is more to creating an extension then just modifying the source code.

Click through for Kevin’s lessons learned.

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Troubleshooting a Fabric Copy Job Connection Closure

Published 2026-09-14 by Kevin Feasel

Reitse Eskens debugs an issue:

This is a very common setup that usually works just fine.

Yet, I ran into something weird that I wanted to share with you. When I set up this copy job, I could reach the database, preview the data, and create the job. No issues. But when I started the actual data transfer, the job ran for two minutes and thirteen to sixteen seconds before throwing an error. On every table.

Click through for the error message, the cause of the error, and how to solve it.

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Monitoring Microsoft Fabric Capacity Usage

Published 2026-09-14 by Kevin Feasel

Gilbert Quevauvilliers performs some measurements:

Before using Fabric Capacity operation events, you need to have a Fabric capacity.

In my testing, Capacity Operation Events consumed approximately 230,000 CUs per day.

Based on my usage, an F4 capacity was required to run this continuously each day.

NOTE: This is based on my usage. Your consumption may be slightly higher or lower.

That’s a fair amount of capacity to track how much capacity you’re using.

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What “Clean Data” Means in Microsoft Fabric

Published 2026-09-11 by Kevin Feasel

Christian Henrik Reich lays it out:

You might have heard countless times that AI needs high-quality and clean data to work properly. However, this is often not accompanied by an explanation of what it actually means. In this post, I will explain how you can achieve this in Microsoft Fabric.

I’ll explain the foundation for how you can prepare your data so your solution can answer business questions, either through reporting or AI.

Clean data alone is not enough. Data also needs to be structured, understandable, and accessible in a way that allows your solutions to answer business questions reliably.

The fancy buzzword that describes a lot of what Christian covers is “ontology” but I appreciate this more detailed description versus relying on a buzzword as a crutch.

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Schema Locks and Time Travel in Fabric Data Warehouse

Published 2026-09-10 by Kevin Feasel

Louis Davidson continues a comparative analysis:

When reading that title, you probably want to get to concept of time travel first, because if you could really do that, you wouldn’t have any more backup and recovery problems. The time travel I will cover is about getting to past versions of data in your data warehouse tables. This is done in a similar way to what you can do with temporal tables in SQL Server, but also VERY different.

The schema locks section is a wee bit of a follow on to Part 4 where I covered isolation where I showed how updating data was handled concurrently. In this post I wanted to briefly finish that conversation and note how it works when you have schema locks.

Most of this behavior is similar to what we have in SQL Server on-premises, though time travel does have a couple of nuances to it versus the box product.

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Mirroring Data from SQL Server into Microsoft Fabric

Published 2026-09-09 by Kevin Feasel

Greg Low has a guide:

Mirroring in Microsoft Fabric is useful when you need to make SQL Server data available in Fabric. Perhaps in OneLake, for example, if you need to use the data for reporting, analytics, notebooks, or another Fabric workload.

However, it’s not suitable for everything, as I’ll explain in this complete guide to mirroring data from SQL Server to Microsoft Fabric.

Before we get started, a note about compatibility. Since SQL Server 2005, there’s been a type of database mirroring totally unrelated to Microsoft Fabric mirroring. I wish these types of terms weren’t so overloaded but, in this article, when I say mirroring, I’m talking only about Fabric mirroring.

Click through for a FAQ-style introduction.

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Implementing SCD Type 2 in Microsoft Fabric

Published 2026-09-08 by Kevin Feasel

Nikola Ilic reminds us that the Kimball model is alive and well:

Slowly Changing Dimensions (SCD) are one of the fundamental concepts in dimensional modeling. If you are not sure what dimensional modeling is, I suggest you first check the series of articles I wrote on data modeling some time ago.

And, when it comes to SCD in particular, SCD Type 2 is rightly considered “the queen” of the SCDs for analytical workloads. Without going into details (since the main goal of this article is to show you HOW to implement the SCD Type 2 in Microsoft Fabric), I’ll just briefly introduce the general concept behind the SCD Type 2.

Click through for a primer and examples in the warehouse and lakehouse.

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Spark SQL Temporary Views on Fabric Schema-Enabled Lakehouses

Published 2026-09-03 by Kevin Feasel

Gerhard Brueckl wants to create a temporary view:

Some time ago my friend Christian Henrik Reich blogged about how to handle schema-enabled lakehouses in Spark temporary views. We already found a good solution leveraging SQL USE keyword to set the schema once and reference tables by name only in our views. However, after some tests, in particular with notebookutils.notebook.runMultiple, I realized that there are some more things to consider as suddenly my SQL and notebooks stopped working when executed in parallel!

But let set the scope for this blogpost first. We recently built a data platform on Microsoft Fabric where we integrated data from different source systems which were combined into a single schema-enabled lakehouse where each source system had it own schema. Naturally, when querying those schemas, we used temporary views to express our business logic in SQL and then continue working with PySpark. We relied heavily on USE to work around the issue describe by Henrik until we realized that this approach does not work in combination with runMultiple. So we had to find another solution which I will describe here.

Read on for that solution.

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Atomicity and Isolation in Fabric Data Warehouse

Published 2026-09-03 by Kevin Feasel

Louis Davidson receives a surprise:

Ironically, today’s topic is kind of the opposite. I expected things to be far different in Fabric, but it isn’t really that different, at least not in behavior, except when it is. Since this system is Parquet file based, I didn’t think there would be locking, blocking, etc. I sort of expected it would be sort of locked down when writing data, maybe just single threaded per file, but highly concurrent when reading. Reads would most likely work like time travel and read previous data where it existed. And transactions? Would there be transactions? I guessed not before I got started with it.

Was I wrong?

Read on for the answer. The concurrency model isn’t exactly the same as SQL Server’s, but it’s not that far off.

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Referencing Assets in Power BI Reports via OneLake URLs

Published 2026-08-31 by Kevin Feasel

Chris Webb wants to load some images:

Several years ago I wrote a very popular blog post about how to store images for your reports inside your Power BI semantic model. It solved the problem of how you could use display images (for example of products) inside your reports without making those images available via a public URL or personal OneDrive Embed Codes. I was very proud of how efficient the M code to do this was but the code was complicated and storing images as text inside a semantic model makes refreshes a lot slower and increases the size of your semantic model in memory, so it’s not ideal. The good news is that, if you have enabled Fabric in your tenant, the August 2026 release of Power BI brings a much better way of solving this problem: you can now store your images (and indeed other files) inside OneLake and reference them from there. This means you can store your images in a secure location, alongside all of your other data, and make them available for use in Power BI. What’s more this doesn’t just work for images, it also works for other types of files such as GeoJSON files used by map visuals.

Click through to see how.

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