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Author: Kevin Feasel

Thoughts on a Cloudless World

Published 2026-06-10 by Kevin Feasel

Mike Donnelly has some tongue-in-cheek responses:

There are some serious angles to this topic, and I have had conversations with people at conferences who are doing a remigration from the cloud, but it feels like the exception not the rule. It is interesting to think about. I spent most of my career working with on-prem SQL Server, but there was a period of about 10 years (the consulting years) where I didn’t touch anything that wasn’t in the cloud. The past several years have been working in a hybrid environment, but most of the work has been moving things to Azure and Fabric. Koen has some prompts for what our blog posts could be about, but rather than dive deep into any one thing I’m going to go with the blog writer’s best friend – a top 10 list.

The funny thing is, in my time as an on-premises DBA, I never dealt with hardware and didn’t have access to the server room.

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Gotchas for a Move On-Premises

Published 2026-06-10 by Kevin Feasel

Brent Ozar lists three pain points:

For T-SQL Tuesday #199, Koen Verbeeck posed an excellent question: if your company moved up to the cloud, but after migrating, had to come back on-premises, what would be the big problems?

I’ve had clients in this exact situation! Here are some of my favorite gotchas from those experiences.

Click through for those three. I’d say that the first one is a major issue and will probably be one for the next couple of years—unless the bottom drops out sooner than I expect and we suddenly have a rush of used hardware from highly unprofitable organizations hit the market.

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Skills for Cloud-to-On-Prem Migration

Published 2026-06-10 by Kevin Feasel

Reitse Eskens focuses on a set of skills:

This month, Koen Verbeeck invites the blogging community to write about their thoughts on returning to on-premises. What could be struggles, things we have to re-learn, etcetera.

When I read the invite, it immediately sparked inspiration, because there are increasing rumours around cloud exits. People musing about ‘what if’. Some clients reference these questions, but so far no one has directly asked me one with the intent of moving forward with it.

Click through for Reitse’s thoughts.

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The Importance of Working with People

Published 2026-06-10 by Kevin Feasel

Mark Wilkinson hits migration from a different angle:

I haven’t written a blog post (here) in over 5 years, so what better time to start things back up than a T-SQL Tuesday? Big shout out to Koen Verbeeck for hosting this month, and picking a great topic: Back to on-prem?

As someone that just ended a 10+ year stint managing a hybrid environment, this topic is very near and dear to me. I went back and fourth on what to write about for this one. There are a lot of great topics. Reliability and observability almost won out but instead I landed on maybe an unexpected topic: soft skills.

Regardless of whether your company is fully on-prem, fully in the cloud, fully hybrid, or fully without a clue, Mark’s advice hits home. And is also one of those things I’ve struggled with.

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Generating Local Text Embeddings in SQL Server 2025

Published 2026-06-09 by Kevin Feasel

Greg Low continues a series on local text embeddings:

In the first article of this series, I explained how to install and configure Ollama to host text embeddings models locally. I also demonstrated how to install Caddy as a proxy to allow SQL Server to use Ollama via https-based calls. In this article, I’ll show you how to make use of this at the SQL Server end.

Greg mentions a few embedding models, but the one I’m pushing right now is Microsoft Research’s Harrier OSS v1 model, specifically the 600m parameter version. It does extremely well in the MTEB leaderboards (the 27b variant is at the top of the board as of June 2026) and has a permissive license. It generates vectors in 1024 dimensions, so the embeddings are a bit chunky, taking up 4kb apiece. But the results are really good.

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Bloom Filters with Valkey

Published 2026-06-09 by Kevin Feasel

Jay Miller checks for a record:

A bloom filter is a small, probabilistic data structure designed to answer one question: “Have I seen this item before?” It provides two potential answers: Absolutely Not, and Probably.

You may think that 100% Yes or No would be better but here’s the thing, probably is really fast and you’re really concerned about the Absolutely Not’s taking up unnecessary connections.

The article speaks to Aiven’s implementation of a Bloom filter in Valkey, but does get into some neat details on bloom filters in general. And if you want to go further down that route, Paul White explains how SQL Server uses Bloom filters.

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Testing SQL Code in Python

Published 2026-06-09 by Kevin Feasel

Jamal Hansen writes some tests:

I once had a query that ran fine for months. Then someone added a column to the source table and a SELECT * downstream started returning unexpected data. The query didn’t error. It just silently gave wrong results. A test would have caught it immediately.

Schema changes break queries silently. Refactoring a CTE can shift results in ways you don’t notice. New data patterns expose assumptions you didn’t know you made. SQL deserves the same testing discipline as the rest of your code, and Python makes it straightforward.

PyTest, the library Jamal uses here, is one of my favorites for this kind of work. You can build up tests without a lot of ceremony and it’s pretty easy to deal with for most use cases.

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The Pain of Moving Indexes between Filegroups

Published 2026-06-09 by Kevin Feasel

Erik Darling explains a process:

At some point you’re going to want to move some indexes to a new filegroup. Maybe you’re separating data across storage, maybe you’re cleaning up after someone who put everything on PRIMARY and walked away, maybe you’ve got your reasons and they’re none of my business.

Whatever the cause, you’d think this would be a solved problem in a database that’s been around since the Clinton administration.

It is not.

Some days, I’m convinced that the only way to win is not to play at all. Erik explaining how to migrate LOB data across filegroups fits that bill perfectly.

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A Primer on Apache Iceberg

Published 2026-06-08 by Kevin Feasel

Walt Riberio lays out an explanation:

Data lakes had a reputation problem. The promise was compelling: dump all your data into cheap object storage—S3, GCS, Azure Blob—and query it whenever you need. The reality was a mess of stale partitions, schema drift, and silent data corruption caused by unsafe concurrent writes. Engineers knew the risks and worked around them rather than fixing them.

Apache Iceberg was built to fix that. And it’s catching on fast—even in the Kafka® world Iceberg is a hot topic. If you’re running ClickHouse® or building a pipeline that feeds into it, Iceberg is quickly becoming hard to ignore if you care about accuracy at scale. It’s becoming the connective tissue of the modern data stack.

Click through to learn more about the product and where it potentially fits in an organization.

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