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Curated SQL Posts

A Primer on OneLake Security

Published 2026-07-07 by Kevin Feasel

James Serra takes us through the different security models in Microsoft Fabric:

The idea behind Fabric OneLake Security (which GA’d on April 2026) is to centralize data access controls at the data layer, rather than configuring security separately for every Fabric experience. You define security once, close to the data in OneLake, using roles that can control access at the folder, table/object, row, and column levels through object-level security (also called Table-level and folder-level security), row-level security (RLS), and column-level security (CLS). Those rules are then enforced by supported Fabric engines and access paths, such as Lakehouse, Spark notebooks, the SQL analytics endpoint in user identity mode, and Power BI Direct Lake semantic models. Downstream experiences that go through those governed paths, such as Power BI reports or Excel connected through the semantic model, inherit the same secured view of the data.

However, OneLake security is not the native security model for every data location in Fabric.

Read on to see which components use what security models, as well as some hints as to the vision for Microsoft Fabric’s ultimate security model.

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A Primer on Microsoft Fabric for SQL Server Professionals

Published 2026-07-07 by Kevin Feasel

Kevin Chant gives the low-down on Microsoft Fabric:

This post covers how you can spread your SQL Server wings with Microsoft Fabric in 2026. As part of a long-running series of posts about spreading your SQL Server wings with the Microsoft Intelligent Data Platform.

Just after Microsoft Fabric was publicly announced during Microsoft Build 2023, I published a post that covered spreading your SQL Server wings with Microsoft Fabric.

A lot has changed since then. Including Microsoft Fabric becoming generally available and the introduction of more workloads. Since Data Days is currently taking place, I decided to publish an updated version.

There’s a lot that has changed in the product, meaning that if your experience with it was how it looked in early 2024, it’s a different world now.

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When Additional Data Doesn’t Shrink Confidence Intervals

Published 2026-07-06 by Kevin Feasel

John Cook follows Betteridge’s Law of Headlines:

In general, new information reduces your uncertainty regarding whatever you’re estimating. The posterior distribution becomes more concentrated as more data are collected.

That’s what happens “in general” but does it necessarily happen every time you get new data? Conceivably if you get surprising data, data that is very unlikely given your current prior, posterior uncertainty might increase.

Click through for an example, as well as a pair of good comments on the post.

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Power BI: Database Was Evicted to Balance the CPU Load

Published 2026-07-06 by Kevin Feasel

Chris Webb diagnoses a case of passive voice:

A few months ago I wrote about a rare error – the “Maximum allowable memory allocation” error – that may occur when the physical machine, or node, that a semantic model is running on in the Power BI Service comes under memory pressure. Recently, someone I was working with who was doing some load testing showed me a related error:

The operation was canceled and the database was evicted to balance the CPU load on the node. Please try again later.

Read on to see what causes this.

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Ways Data Apps Improve Microsoft Fabric Semantic Models

Published 2026-07-06 by Kevin Feasel

Ruben Van de Voorde digs into data apps:

Fabric data apps are the most interesting thing to happen to data visualization in Power BI and Microsoft Fabric, ever. They provide limitless potential for how to visualize and interact with your data. However, they also indirectly provide a major benefit to your semantic models by allowing them to be simpler to build, use, and maintain.

In this article, we argue that data apps provide a reporting layer that is separate from the semantic layer, something that wasn’t true in Power BI due to tight coupling between reports and semantic models.

Click through to see where they fit vis-a-vis Power BI reports and semantic models.

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Ghost Records in SQL Server

Published 2026-07-06 by Kevin Feasel

Martyn Jones sees dead records:

When a delete operation is executed, or when an update causes a page split, a marker is set in the page header, and each row is marked to show that the record is to be deleted; this is the process of creating ghost records. The concept is to improve performance as the data is not immediately removed, and, with row-level locking, it helps minimise blocking and supports efficient concurrency under typical isolation levels such as Read Committed and above. If a transaction is later rolled back, the cost of effectively restoring the row is considerably reduced as the data doesn’t need to be reinserted, only the marker removed. Later demos will show this in action.

This is the first part of a longer series on ghost records and ghost cleanup.

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Building a Lakehouse Table Health Pipeline

Published 2026-07-02 by Kevin Feasel

Gilbert Quevauvilliers builds a pipeline:

In this post I will show how I used GitHub Copilot CLI / Agent mode in VS Code to create a Microsoft Fabric pipeline that checks Lakehouse table health and only optimizes tables that require maintenance. I’ll also show the prompts I used, the issues I ran into, and how Copilot helped me resolve them.

Recently Microsoft announced Lakehouse table health. The post showed how you can check can check the health of your Lakehouse table in a very simple way.

Read on to see what this feature can do, as well as how Gilbert was able to gin up (with the help of GitHub Copilot) a pipeline to optimize lakehouse tables. What I appreciate is that Gilbert also included the errors in the process, rather than making it seem like everything with perfectly the first time around.

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SSMS Tools in VS Code

Published 2026-07-02 by Kevin Feasel

Denny Cherry has a new VS Code extension:

Are you a user of VS Code, but you wish that the MSSQL extension had more of the SQL Server Management Studio features? Good news, there’s now a VS Code Plugin that gives you those capabilities in VS Code by using the “SSMS Tools for SQL Server” extension.

Click through to see what it includes. There’s a pretty decent amount of existing SSMS functionality in this extension, especially on the database administrator side, where Azure Data Studio and VSCode historically have not focused.

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Error 1408 with Availability Group Automatic Seeding

Published 2026-07-02 by Kevin Feasel

Sean Gallardy troubleshoots an error:

This used to be a very hot topic around SQL Server 2016 when automatic seeding first came out, then everyone learned how automatic seeding was just manual seeding but done for you and the topic went away. A decade later, it’s somehow back to being a hot topic. I guess all that is old is new again.

Error 1408 states that the database doesn’t have enough log to find a starting point with the primary/principal – which, let’s just use AG terminology from this point on because mirroring has been dead for 14 years and counting.

Mirroring has been dead and yet it lives on in undead form. Regardless of that, Sean provides some good information around what automatic seeding actually means and what you can do if you get stuck with Error 1408.

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An Introduction to Spindle

Published 2026-07-02 by Kevin Feasel

Jonathan Stewart has an open-source tool:

A client wanted to see a feature that I had previously built for another customer. I obviously couldn’t just use real client data. When I used Faker and other tools like that, I got data that looked fine on its own but fell apart when I needed relational integrity such as needing an order to point to a customer which points to an address. AdventureWorks, Contoso, WideWorldImporters and the like are great for what they are, but they are either too small, too clean or not relevant to the demo.

I needed data that would not only look the part for the subject, but also follow relationships, have proper distributions, and so on.

So I built Spindle.

I’ve used Spindle for work purposes. It’s a good product for generating artificial data, either from one of the default data domains (e.g., HR, insurance, health care, manufacturing) or based on an existing database schema.

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