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Author: Kevin Feasel

Migrating SQL Server Service Accounts to gMSA

Published 2026-03-17 by Kevin Feasel

Deepthi Goguri takes advantage of Group Managed Service Accounts in Windows:

A Service Account is something that applications like the SQL Server, IIS, or scheduled tasks need to run under using Microsoft Active Directory. These are the regular domain user accounts, where the passwords needs to be manually managed and rotated. As these needs to be manually updated, downtime to the services are required if the password needs to be changed. Not only that but syncing these passwords across multiple servers can be an issue. This problem is resolved by using the Standalone Managed Service Account as Windows can manage the password automatically.

Read on to learn more about single MSAs and group MSAs, and some tips and limitations.

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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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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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Personalizing the Command Line in Linux

Published 2026-03-16 by Kevin Feasel

Thomas Williams messes with the command line:

This post is part 1 of quality-of-life changes I make when I first log in to a new Linux server. I primarily use bash (though I also have zsh set up on some machines):

  • .hushlogin
  • .inputrc
  • .vimrc
  • .bashrc

Click through for some examples of customization. I’m not sure if Thomas will get to this in the series, but I like using powerline for more advanced customization.

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The Cloud (Alone) Is Not a Disaster Recovery Strategy

Published 2026-03-16 by Kevin Feasel

Umair Shahid explains that you need more:

When AWS stumbled – twice – in October 2025, many teams discovered that “we are in the cloud” is not the same as “we have disaster recovery”.

Applications went offline, customer-facing portals returned errors, and internal dashboards that teams rely on every morning failed to load.

Most of those systems were already running on managed cloud services. They had multi-AZ databases, auto scaling groups, and health checks. What they did not have was a clear answer to three simple questions:

Read on for those questions, which are critical to ensuring business continuity.

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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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Troubleshooting a Vanishing CU Install

Published 2026-03-16 by Kevin Feasel

Sean Gallardy digs into an issue:

Someone asked if I’ve ever had a CU install where you run it and it goes through the extraction process, then right as it hits 100% just exists and nothing happens. Well, that’s pretty weird, and no, I hadn’t. I was, however, intrigued! Since I love my readers, I made a repro of what the person this was occurring to, saw.

Click through for the expectations and what it actually turned out to be.

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No Buffer Pool Memory or OS Memory Available

Published 2026-03-16 by Kevin Feasel

Paul Randal re-tells a story:

Jonathan had a client issue recently where SQL Server’s buffer pool had been forced down to a ridiculously small size, only a few hundred MB, but the OS also showed basically no free memory. Page Life Expectancy was zero! What was going on?

From investigating SQL Server’s memory usage, the memory manager showed that target and total memory were the same, at only 1.2GB, and lock pages in memory was correctly set.

Read on for Jonathan’s troubleshooting steps and what he discovered.

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Loading Multiple CSV Files in R

Published 2026-03-13 by Kevin Feasel

Stephen Royle has a set of challenges:

In a previous post, I described how to run a session to teach R to cell biologists. In this post we’ll look in a bit more detail at one of the steps: how to load data into R.

As a reminder, a typical analysis task in cell biology follows these steps:

  • do the experiment(s)
  • collect the data – e.g. microscopy images
  • analyse and get a plain text (csv) output – e.g. using Fiji
  • load the data into R
  • crunch some numbers and plot

Click through for some explanation, as well as a few exercises (and solutions) for people trying to learn the language. H/T R-Bloggers.

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