A Fine Slice Of SQL Server
I see these issues all the time. Developers create applications that require specific collations at the server level, because they don’t handle temporary tables (and by extension) tempdb well.
All this can be avoided, and you can easily build applications that will work with temporary tables, without worrying about the server (and tempdb) collation.
Read on to learn more about the problem and what you can do to mitigate it.
Leave a CommentJeff Iannucci wants to smooth things out:
While reviewing the configuration of a client’s SQL Server instance recently, I noticed that they had a very curious tempdb configuration. Although it had multiple files for the eight CPU cores as Microsoft recommends for tempdb configuration, it also had another MUCH larger file with a MUCH larger growth rate.
And it turns out that when this happens, there are some knock-on effects that make it a challenge to remove such a file later. But read on for the solution.
Leave a CommentMarco Russo clues us in on a bug:
In mid-January 2026, many Power BI Desktop users began noticing a strange visual regression: several icons in the interface—including the Data, Model, and DAX icons—appeared as solid black shapes instead of their normal outlines. The issue appeared suddenly, even on machines where Power BI Desktop had not been updated. Because it happened the same week that Power BI Desktop January 2026 was released, this led to understandable confusion.
The root cause was not Power BI Desktop itself but an unexpected regression in the WebView2 runtime, one of the external dependencies used to render parts of the UI. Microsoft confirmed the issue and is preparing an official fix.
Marco does have a temporary fix that works until Microsoft has the official fix in place.
Leave a CommentRajesh Vakkalagadda needs a sample:
Sampling is a fundamental process in machine learning that involves selecting a subset of data from a larger dataset. This technique is used to make training and evaluation more efficient, especially when working with massive datasets where processing every data point is impractical
However, sampling comes with its own challenges. Ensuring that samples are representative is crucial to prevent biases that could lead to poor model generalization and inaccurate evaluation results. The sample size must strike a balance between performance and resource constraints. Additionally, sampling strategies need to account for factors such as class imbalance, temporal dependencies, and other dataset-specific characteristics to maintain data integrity.
Click through for an answer in Scala. The Python implementation would be very similar,
Leave a CommentVincenzo Cola announces a new R package:
After passing through rOpenSci peer review, the distionary package is now newly available on CRAN. It allows you to make probability distributions quickly – either from a few inputs or from its built-in library – and then probe them in detail.
These distributions form the building blocks that piece together advanced statistical models with the wider probaverse ecosystem, which is built to release modelers from low-level coding so production pipelines stay human-friendly. Right now, the other probaverse packages are distplyr, allowing you to morph distributions into new forms, and famish, allowing you to tune distributions to data. Developed with risk analysis use cases like climate and insurance in mind, the same tools translate smoothly to simulations, teaching, and other applied settings.
Click through for an overview of the package.
Leave a CommentPaul Turley thinks about how we think:
You can be the greatest report designer on the planet, but if your report doesn’t meet the needs of the report consumer, it’s all for nothing. In this section, I break down the most important considerations for identifying your audience and their information needs. These are all factors to consider before you jump in and start designing your report.
Paul hits on quite a few of the foundational concepts around how humans visual stimuli and tells some interesting stories along the way.
Leave a CommentMark Beaumont diagnoses an error:
Recently, one of our clients encountered an issue while running a data update in SQL Server. The operation failed immediately with a configuration error, specifically targeting Large Object (LOB) data:
Length of LOB data (169,494) to be replicated exceeds configured maximum 65,536. Use the stored procedure sp_configure to increase the configured maximum value for max text repl size option, which defaults to 65,536. A configured value of -1 indicates no limit, other than the limit imposed by the data type.
The tricky part was, that client wasn’t using replication. Read on to learn about the culprit.
Leave a CommentAaron Merrill announces a new preview offering:
Microsoft Fabric continues to expand the OneLake security surface with new granular REST API support for role management, giving developers and platform teams far more control over how security policies are created, retrieved, and managed programmatically. In addition to the existing batch role API, Fabric now offers discrete Create, Get, and Delete role APIs, making it easier to build incremental, automation-friendly security workflows that align with modern DevOps and governance practices.
Click through for a quick explanation of how things did work and how they will work going forward.
Leave a CommentJohn Deardurff shares some advice:
After 25 years as a Microsoft Certified Trainer (MCT), one thing I have learned is that teaching technical content requires more than just subject‑matter expertise. Great technical instructors create an environment where learners feel comfortable, engaged, and motivated to explore complex concepts at their own pace.
Click through for ten such tips. I tend to follow seven of them pretty well, though the three around questions are where I’m weakest.
Leave a CommentIn this article, you will learn how to package a trained machine learning model behind a clean, well-validated HTTP API using FastAPI, from training to local testing and basic production hardening.
Topics we will cover include:
- Training, saving, and loading a scikit-learn pipeline for inference
- Building a FastAPI app with strict input validation via Pydantic
- Exposing, testing, and hardening a prediction endpoint with health checks
Let’s explore these techniques.
I definitely enjoy how simple it is to use FastAPI.
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