A Fine Slice Of SQL Server
Thierry Moudiki checks an interval:
A previous post introduced the
crossvalidationpackage for R. This time, the focus is on probabilistic forecasting — evaluating not just how accurate point forecasts are, but how well-calibrated prediction intervals are, using empirical coverage rates and Winkler scores – andcrossvalidation.
Click through for the code and not much additional commentary. H/T R-Bloggers.
Leave a CommentFabiano Amorim describes a security concern:
This article describes a restore-boundary weakness involving indexed views. An attacker prepares a database backup on an attacker-controlled instance, tampers with the persisted definition of an indexed view, and delivers that database through an otherwise ordinary backup-and-restore workflow.
After the restore, SQL Server evaluates the preserved metadata during indexed-view optimizer-driven execution. Data from databases the attacker cannot directly query may still be pulled into the attacker’s own restored database through trusted internal processing paths. This is a clear cross-database confidentiality problem.
It’s an interesting post. The scope of damage is somewhat limited considering that the attacker would need legitimate permissions to the instance, but something to keep in mind nonetheless.
Leave a CommentJohn Deardurff takes a look at a new capability in SQL Server 2025:
Data rarely arrives in perfect condition. Typos, regional spelling differences, and inconsistent formats make exact matching unreliable in real-world scenarios. That’s where fuzzy matching comes in; and SQL Server 2025 introduces powerful built-in functions to handle it directly in T-SQL.
None of the functions are particularly novel, but it is nice to have them directly available in SQL Server, especially because Integration Services (where some of this functionality lived) has been on life support for a decade.
Leave a CommentGarry Bargsley continues a series on Powershell capabilities:
Welcome back to PowerShell Strikes Back. We’re three weeks in, and the training is paying off. In Week 1, we learned that quotes are not interchangeable. In Week 2, we put variables to work – storing server names, config values, service objects, and boolean results. If you’ve been following along and running the examples in your own environment, you’re already writing better PowerShell than you were a month ago.
This week, we tackle the concept that transforms a script from a one-time operation into an actual tool: the ForEach loop.
Garry also ties in error handling, which is important during loop iteration.
Leave a CommentJamal Hansen shows off a capable in-memory analytic database:
This is a real-world example of a common data engineering pattern. You may have heard of ETL (Extract, Transform, Load), where data is transformed before it reaches its destination. What we are actually building today is the more modern variant, ELT: Extract, Load, Transform.
Read on for the process. I like DuckDB a lot and this is one of the types of use cases in which it excels.
Leave a CommentMeagan Longoria wraps up another T-SQL Tuesday:
Thank you to everyone who participated in T-SQL Tuesday #198! When I wrote the invitation post, I intentionally kept the prompt broad because change detection looks different depending on your source system, your infrastructure, your data volumes, and what you need to do with the changes once you have them. The responses covered SQL Server internals, Microsoft Fabric and Synapse, hashing strategies, metadata-driven frameworks, and Synapse workspace diffing with Python. Here’s a summary of each contribution.
Read on for links to eight responses.
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