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Category: Microsoft Fabric

Connection Recency in Fabric REST APIs

Published 2026-09-18 by Kevin Feasel

Ryan Adams explains a concept:

Connections are shared infrastructure in Microsoft Fabric. Pipelines, dataflows, semantic models, and other Fabric items use them to reach data sources without storing connection details and credentials in every item. Connection Recency adds the context administrators need to understand how each connection is being used. If you go to “Manage Connections and Gateways” in Fabric settings and open the settings of any connection, the following information card will populate:

Click through to learn more about connection recency and how it can be helpful to understand the mechanic.

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CU Consumption after the Query Finishes

Published 2026-09-17 by Kevin Feasel

Nikola Ilic answers a question:

After my article on Fabric Warehouse CU metering, which caused a lot of people reaching out and mentioning that it helped them understand the topic better, I decided to cover SQL database in Fabric from a similar angle too.

Imagine opening an internal application, updating a few records, and closing it two minutes later. You check consumption afterward and see that compute continued accruing.

Before blaming the application, it helps to understand what keeps the database online, and what Fabric actually measures.

Read on for that answer.

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A Review of SQL Database in Microsoft Fabric

Published 2026-09-17 by Kevin Feasel

Paul Turley shares some thoughts:

When I first learned that SQL Server databases were coming to Fabric, I wasn’t entirely sure how and why we would use it. Now that I’ve had a chance to use SQL Database in Fabric, I wanted to share some of my experience and thoughts about where and how it makes sense to use it. I thought: Fabric is an analytics platform, and we primarily store data in a Lakehouse or warehouse to prep it up for reporting and analysis with Power BI. Why would I want to store data in a SQL Server database in the Fabric SaaS environment? I’ve since implemented “SQL Database for Fabric” (aka “Fabric SQL Database”) in several projects and I’m just blown away by how fast and simple it is to use. In this post, I will dig into the cost and performance and use cases where this option makes the most sense compared to other hosted implementations of the SQL Server database engine.

Click though to learn more.

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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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