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Author: Kevin Feasel

Thoughts on the Death of the DBA

Published 2026-01-15 by Kevin Feasel

Rebecca Lewis presses X to doubt:

Every few years, something comes along that’s definitively, no-questions-asked going to replace us.

Let’s review the historical record.

Rebecca points out a half-dozen instances in which people have decried the end of the DBA role, and yet it’s still there somehow… And honestly, you could probably find a half-dozen more examples without searching too hard, like how SQL Server 2000 was going to render DBAs obsolete because of its self-management capabilities. Which, admittedly, is very similar to the 1996 Oracle announcement.

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MinIO Alternatives

Published 2026-01-15 by Kevin Feasel

Robin Moffatt looks for alternatives:

In late 2025 the company behind MinIO decided to abandon it to pursue other commercial interests. As well as upsetting a bunch of folk, it also put the cat amongst the pigeons of many software demos that relied on MinIO to emulate S3 storage locally, not to mention build pipelines that used it for validating S3 compatibility.

In this blog post I’m going to look at some alternatives to MinIO.

Read on for Robin’s analysis of a half-dozen alternatives.

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Defining Applications in Power BI and Microsoft Fabric

Published 2026-01-15 by Kevin Feasel

Andy Brownsword deploys an app:

When using Power BI or Fabric workspaces to browse reports, we’re greeted with a list of items and their attributes. While attention is given to report visuals such as bars, pies, candles, and RAG highlights, the surrounding experience is neglected. When it comes to consumption, the standard interface falls short.

Apps fill this gap. They’ve been around in Power BI for a while, but with the additional layers that come with Fabric, the need for a clean way to present content is increasingly valuable.

Read on to learn about more functionality around apps and how you can set them up in Fabric/Power BI.

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Lessons Learned in a SQL Server 2025 Upgrade

Published 2026-01-15 by Kevin Feasel

Aaron Bertrand shares some lessons learned:

We recently upgraded multiple systems to SQL Server 2025. The engine upgrade itself was smooth, but three unexpected issues surfaced in our lower environments as we planned out production. None of these issues prevented the upgrade from completing, but all three could easily derail an otherwise smooth in-place upgrade to SQL Server 2025. What were these issues, and how can you avoid hitting them?

My biggest surprise out of this is that full-text search actually got upgraded.

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SQL ConstantCare Population Report for Q4 2025

Published 2026-01-15 by Kevin Feasel

Brent Ozar has an update:

It’s time for our quarterly update of our SQL ConstantCare® population report, showing how quickly (or slowly) folks adopt new versions of SQL Server. In short, people are replacing SQL Server 2016 and 2017 with 2022!

I do think that 2025 will pick up steam. The marginal change was mostly into 2022, but 2025 wasn’t officially released until November and I’m guessing not many companies upgraded in December. I do think we’ll see some pickup of SQL Server 2025 in this quarter.

As always, this is my throat-clearing reminder that what Brent has is a biased sample of the SQL Server population and is not necessarily reflective of the population as a whole. It’s a very interesting sample upon which to reflect, but its specific bias is that it necessarily only includes customers of Brent Ozar’s service, which will be a specific subset of organizations.

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Indexes and COUNT() in SQL Server

Published 2026-01-15 by Kevin Feasel

Louis Davidson does some testing:

A few weeks ago, there was a LinkedIn post (I can’t find it anymore) that covered something about how indexes were used by COUNT in SQL. I think it may have been based on SQL Server, but I am not sure (it is rare that one of the SQL posts on LinkedIn mentions a platform). At the time, I went and tried a few of the mentioned cases and realized this was an interesting question: how does the COUNT aggregate use indexes when you use various different expressions.

Louis has a series of test cases and I got most of them right, though I wasn’t sure about one particular optimization.

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OneLake Diagnostics Immutability Generally Available

Published 2026-01-14 by Kevin Feasel

Tom Peplow makes an announcement:

In October 2025, we introduced OneLake diagnostics—a powerful capability that helps teams “answer who accessed what, when, and how” across your Fabric Lakehouse environment. OneLake diagnostics streams JSON-based activity logs into a Lakehouse you choose, enabling rich analysis, governance, and compliance workflows. A powerful capability that helps teams “answer who accessed what, when, and how” across your Fabric Lakehouse environment. OneLake diagnostics streams JSON-based activity logs into a Lakehouse you choose, enabling rich analysis, governance, and compliance workflows.

We are strengthening that foundation with the introduction of immutable diagnostic logs—a new capability that ensures diagnostic events cannot be altered or deleted for a defined retention period, giving you tamper-proof data for the entire lifecycle of your logs.

I do like the idea, but beware the additional costs: immutable also means you can’t delete it later, so 10 years from now, you’re still going to be paying for this diagnostic data.

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Cloud Storage Archival via Parquet Files

Published 2026-01-14 by Kevin Feasel

Joey D’Antoni builds a tool:

What I’m writing about today has nothing to do with analytics, per se. It has everything to do with cloud storage, and the way operations there are priced. Specifically, metadata operations–in the demo code I’ve shared we’re going from five files to one, but you can imagine going from a much larger number of files to much smaller number of files. You may ask–“Joey that sounds dumb, why are you reinventing zip and iso files”. Well, the main reason is that many cloud operations are priced on the number of objects–for example if you had to calculate a checksum across a number of files on S3. (For files/objects that were created before S3 automatically did checksums).

Click through for more information on how it works, as well as a link to the GitHub repo.

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A Deep Dive into PostgreSQL Arrays

Published 2026-01-14 by Kevin Feasel

Radim Marek talks arrays:

The official documentation provides a good introduction. But beneath this straightforward interface lies a set of more complex properties than most of us realise. Arrays in PostgreSQL are not just “lists” in a field. They have their own memory management strategy, their own index logic, and a lot of edge-case scenarios.

As it goes with boringSQL deep-dives, this article will explore the corners of array functionality that might break your production.

Click through for some not-boring explanation around arrays in PostgreSQL.

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