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Curated SQL Posts

Dealing with Multiple Fabric Capacities

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

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

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

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

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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No Buffer Pool Memory or OS Memory Available

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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Troubleshooting a Vanishing CU Install

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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Loading Multiple CSV Files in R

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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Transparent Column Encryption in PostgreSQL

Vibhor Kumar releases an extension:

This extension provides transparent column-level encryption using custom PostgreSQL datatypes so developers can read and write encrypted columns without changing their SQL queries.

And perhaps the most human part of this project is this:

the idea for this project started back in 2016.

It stayed with me for years as one of those engineering ideas that never quite leaves your mind — the thought that PostgreSQL itself could enforce encryption at the column level.

The diagram does look a bit like what you’d see for SQL Server’s Transparent Data Encryption, though the internal mechanics are a good bit different.

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