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Day: September 23, 2026

An Explanation of Fabric Licensing

Published 2026-09-23 by Kevin Feasel

Paul Turley digs in:

When the Power BI cloud service morphed into the Fabric platform a few years ago, Microsoft introduced very attractive cost and capacity options for small organizations, teams and projects – and its easy to scale-up. This is why Fabric works for both small and huge workloads.

Fabric licensing might look intimidating from the outside, mostly because Microsoft describes it in acronyms. Strip those away and it really boils down to two questions.

Click through for those questions and what it all means.

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A Decade of Logical Replication in Postgres

Published 2026-09-23 by Kevin Feasel

Dimitri Fontaine reflects on ten years of functionality:

A long time ago I ran a write-heavy system on a hub and a handful of workers. Each worker took a share of the application traffic and wrote events locally. The hub owned the reference data (customers, plans, prices), pushed it down to the workers, and pulled every worker’s events back up to compute the invoices. The plumbing was Londiste and PgQ: triggers on every table, a queue per node, a ticker, and a Python daemon per hop. It worked, and it was a lot of moving parts to explain to anyone new.

Postgres 10 shipped logical replication in 2017, and 19 is the tenth release that has it. Every release since Postgres 10 has taken a piece of that plumbing and made it a line of SQL.

Click through for a reflection on what’s there and what has changed over the years. This first post in the series covers the hub and worker model.

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Creating Alerts in Microsoft Fabric

Published 2026-09-23 by Kevin Feasel

Koen Verbeeck turns on the Bat-signal:

We are building a new data platform with Microsoft Fabric and we’re using the warehouse for our “gold layer”. We don’t have real-time ingestion or analytic requirements, but we have a use case where we want to send an alert if a certain threshold is reached. Ideally, the alert is sent when a SQL query returns a result set.

Click through to see how that’s possible, as well as what kinds of rules, triggers, and conditions you can set.

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Creating a Security Checklist Based on STIGs

Published 2026-09-23 by Kevin Feasel

Marlon Ribunal builds a checklist:


Here’s a follow up for our US Department of Defense STIG document. In my previous post, SQL Server Security Hardening Guide Using the DoD STIG Checklist, I walked through how I used the DoD STIG checklist as a starting point for reviewing and hardening a SQL Server environment.

After going through the checklist, I started thinking about what I would actually want to use the next time I perform a security review.

The DoD STIG for SQL Server is a great, solid starting point for establishing your security practices with SQL Server. In fact, it’s also a good template for your own STIG in your organization. So, you may want to create a custom checklist that makes sense from the perspective of your SQL Server environment.

The STIG is detailed, which is a good thing, but I found myself wanting something a little more practical for day-to-day DBA work. Something I could open, work through one item at a time, record what I found, and come back to later without having to navigate through the entire STIG document every time.

Click through to see what Marlon came up with.

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Contrasting LTAP and HTAP

Published 2026-09-23 by Kevin Feasel

Paul Andrew notes another convergence of OLTP and OLAP:

At the Data and AI Summit in June this year, Databricks introduced Lake Transactional/Analytical Processing, or LTAP. My first reaction, I’ll admit, was cynicism, just for a change. Every software vendor must invent new names for old things these days. And for those of us who have worked in data architecture long enough to have the grey hair to prove it, like me, this felt very familiar. Hybrid Transactional and Analytical Processing, or HTAP, a term Gartner coined back in 2014, has long advertised bringing operational transactions and analytics “closer together”. In my experience it never really became a strong implementation pattern in data platform deliveries.

Read on to learn how LTAP and HTAP differ, where Databricks and other competitors (like Microsoft) are going in this space, and how much they have yet to do.

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Vector Comparison in SQL Server 2025

Published 2026-09-23 by Kevin Feasel

Chad Callihan demonstrates a vector comparison:

Vectors consist of an ordered list of numbers that represent characteristics or meaning captured from data. That data could be words, images, or other files. An AI embedding model is used to analyze the data and translate it into a long list of numbers that becomes that data’s vector embedding. The closer the vector embeddings are to each other, the more closely related the meanings of the data are together.

While vectors are more commonly hundreds or thousands of numbers, we’re going to simplify it and make up three numbers for our example.

Click through for that comparison.

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