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Author: Kevin Feasel

GPopt for R

Published 2026-07-30 by Kevin Feasel

Thierry Moudiki looks at an R port of a Python package:

Keep in mind that this package is for Machine Learning hyperparameter tuning: the global minimum won’t always be found, but this isn’t an issue, since it means you aren’t overfitting the training set.

It’s ported the same way as nnetsauce for R was: with uv to create an isolated Python virtual environment containing the Python GPopt package, and reticulate to call into it from R. Every function in this R package is a thin wrapper that returns the underlying Python object; the general rule is: object accesses with .’s in Python are replaced by $’s in R.

Click through for the instructions and examples of how it works. H/T R-Bloggers.

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Microsoft Fabric Purview Protection Policies

Published 2026-07-30 by Kevin Feasel

Gilbert Quevauvilliers has a video:

How to use Microsoft Purview Protection Policies so a Fabric workspace Admin can still fully administer the workspace… but is completely blocked from seeing any of the sensitive data inside it.

You keep the person as Admin (so they can manage items, permissions, capacity, etc.) while the protection policy + sensitivity label combination simply removes their ability to open or view the protected Lakehouses, Notebooks, etc.

Click through for the video.

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Challenges with DATE_BUCKET()

Published 2026-07-30 by Kevin Feasel

Erik Darling has a new video:

But what’s also strange here, too, is that SQL Server estimates one row very reliably for a date bucket. So, you know, you might be careful about that as well, especially if you’re returning far more than one row. You might be unhappy with the one row estimate.

It might bring you back to the bad old days of table variables and off histogram values, stuff like that. But, yeah, anyway, I had a point with all that. Let’s do this.

Click through to learn Erik’s point.

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Optimized Locking in SQL Server 2025

Published 2026-07-30 by Kevin Feasel

Louis Davidson tries out a new feature:

A feature I have been trying to find time to write about since it arrived is Optimized Locking, which first shipped in SQL Server 2025. This is a feature that changes the way SQL Server locks manages concurrency in a very interesting number of ways. Most of which, as I start writing this blog, I don’t understand. And I probably won’t when I finish.

What I can promise you is an example rich blog where I examine the impact of this feature.

Read on to see what Louis was able to learn.

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Database Refactoring with VS Code

Published 2026-07-29 by Kevin Feasel

Drew Skwiers-Koballa shows off some functionality:

SQL database projects are a fundamental tool for keeping your database in source control, tracking changes and collaborating on quickly evolving database needs. Not to be forgotten, their superpower is being able to dynamically generate deployment scripts that match the declared state and the exact environment you’re deploying to. SQL project development becomes more challenging when a database needs serious changes, where the requirements of the system have evolved to the point of having to rename tables and move objects between schemas.

This should be pretty familiar to people who have worked with various iterations of database projects in Visual Studio over the years.

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Improving Shiny and RMarkdown Inputs

Published 2026-07-29 by Kevin Feasel

Thomas Williams is back with more:

In my last post on improving inputs in R Markdown/Shiny, I covered four improvements to quality of life in interactive reports and dashboards. In this blog post I have three more, all approaches I’ve used to add professionalism to self-service R Markdown files.

In yet another plug for R Markdown: most of these techniques can be used in a single *.Rmd file, or can be included in many by putting them in a common CSS or javascript file and linking to it.

Click through to see what you can do.

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Huge Table Partitioning

Published 2026-07-29 by Kevin Feasel

Michael J. Swart partitions a rather large table:

Aaron Bertrand wants you to consider using partitioned tables and the sliding window pattern to help archive old data.

That’s a great idea. In fact, I’d like to do that at my own job. I have a truly humungous log table (Terabytes) and its clustered index is already on CreatedDate so it’s a good candidate for this pattern.

Click through to see how, but also one pain point you’re likely to run into when adding partitioning to existing large tables.

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The Performance Impact of Visual Calculations

Published 2026-07-29 by Kevin Feasel

Marco Russo and Alberto Ferrari dig into the nuance:

The goal of visual calculations is to simplify some reports and calculations, rather than to optimize performance. However, it is common sense that – in some scenarios – visual calculations can bring some benefit from the performance point of view.

The main idea is that a report may precompute some values and then, to further elaborate on them, it may use the content of the virtual table rather than recompute the values multiple times.

Read on to learn how you can adjudge what that visual calculation is doing for you in terms of performance relative to alternatives. But hey, if you’re stuck with Power BI Report Server, you don’t have to worry about visual calculations potentially slowing anything down. Mostly because you don’t get visual calculations.

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A Primer on Indexing in SQL Server

Published 2026-07-29 by Kevin Feasel

Ed Pollack has a guide:

Indexes are supposed to make SQL Server faster – so why do so many databases end up slower, bloated, and harder to maintain when they have more of them? It usually comes down to misapplied indexes rather than missing ones. There may be too many that are too wide, tuned with settings that don’t fit the workload, or built on assumptions that stopped being true years ago.

This guide walks through the most common SQL Server index tuning mistakes seen in production environments, such as over-indexing, oversized INCLUDE lists, unnecessary fill factor settings, misuse of SORT_IN_TEMPDB, over-aggressive index maintenance – and the myth that heaps are a shortcut to speed. Features real examples.

I think this serves as a reasonable overview of the topic. You can certainly get into more nuance on a number of the topics, but this is a good starting point.

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