A Fine Slice Of SQL Server
It’s been a while since we made some improvements to the public version of sp_CheckSecurity, but internally we’ve been busy fine tuning checks and adding even more to discover potential vulnerabilities in your SQL Server instances.
Today we’re announcing a new version that includes additions, corrections, and a few other adjustments that should be helpful. Here’s what new!
Read on to see what has changed.
Leave a CommentSumiran Tandon makes an announcement:
Customer managed keys were launched in preview, offering workspace administrators the ability to use keys in Azure Key Vault and Managed HSM, to protect data in certain Fabric items. Now, we are extending the encryption support to more Fabric workloads. You can now create Fabric Warehouses, Notebooks and utilize the SQL Analytics Endpoint in workspaces enabled with encryption using your keys. The changes are rolling out and should be available in all regions over the next few days.
Freddie Santos digs into what this means for Fabric Warehouse and the SQL analytics endpoint:
Fabric already ensures that your data is encrypted at rest using Microsoft-managed keys. But for many organizations—especially in regulated industries—encryption alone isn’t enough. They need the ability to control and manage the keys that protect their data, aligning with internal compliance requirements, regulatory standards, and governance best practices.
I know that there are enough companies where this is absolutely necessary for adoption of a product, but I should point out that even without bringing your own key, Microsoft does use their own generated keys to encrypt your data at rest.
Leave a CommentBrendan Tierney gets comparing:
When comparing text strings we have a number of functions on Oracle Database to help us. These include SOUNDEX, PHONIC_ENCODE and FUZZY_MATCH. Let’s have a look at what each of these can do.
These are some classic word comparison techniques, but they work pretty well in specific circumstances.
Leave a CommentLouis Davidson plays around with the new string concatenation operator:
I was looking around for stuff to post about the other day, so I read through the latest What’s new in SQL Server 2025 and I found this:
|| (String concatenation) Concatenate expressions with expression
||expression.My interest was piqued to say the least. Using + to concatenate string data has always had its issues, and as much as I love the
CONCATfunction, it is kind of clunky to use versus a proper operator.
This is part of the ANSI SQL standard, and that standard does specify most of its behavioral patterns. Read on as Louis tries it out in a variety of circumstances.
Leave a CommentNikola Ilic digs into some announcements:
In the ocean of announcements from the recent FabCon Europe in Vienna, one that may have gone under the radar was about the enhancements in performance and cost optimization for Dataflows Gen2.
Before we delve into explaining how these enhancements impact your current Dataflows setup, let’s take a step back and provide a brief overview of Dataflows. For those of you who are new to Microsoft Fabric – a Dataflow Gen2 is the no-code/low-code Fabric item used to extract, transform, and load the data (ETL).
It sounds like these changes move Dataflows Gen2 from the “Never choose this” option to something that has become viable in at least some circumstances.
Leave a CommentI typically work in quantitative ecology and molecular epidemiology, where we use statistical models to predict species distributions or disease transmission patterns. Recently though, I had an interesting conversation with a data science PhD student who mentioned they were applying GAMs to predict Customer Lifetime Value at a SaaS startup. This caught my attention because CLV prediction, as it turns out, faces remarkably similar statistical challenges to ecological forecasting: nonlinear relationships that saturate at biological or business limits, hierarchical structures where groups behave differently, and the need to balance model flexibility with interpretability for stakeholders who need to understand why the model makes certain predictions.
This is an interesting article and I had not thought of using a GAM for calculating Customer Lifetime Value. I used a much simpler technique the one time I calculated CLV in earnest. H/T R-Bloggers.
Leave a CommentIn this video, I show you various techniques you can use to make window functions faster, including proper indexing and usage of batch mode. I also demonstrate the performance difference between RANGE and ROWS.
This wraps up my series on window functions, and although I pack a lot of content into the video, I highly recommend checking out the links for deeper dives into performance.
Leave a CommentSebastiao Pereira generates random numbers:
Generating random numbers from a normal distribution is essential for accuracy and realistic modeling, simulation, inference, and algorithm design. This is useful for scientific, engineering, statistical, and AI domains. Let’s see how we can create Box Muller transform functionality in SQL Server without the use of external tools.
I’ve used this technique before in SQL Server and it works really well. Performance is fine and the results approximate a normal distribution like you’d want.
Leave a CommentWarda Bibi lays out the basics of high availability and disaster recovery in PostgreSQL:
System outages, hardware failures, or accidental data loss can strike without warning. What determines whether operations resume smoothly or grind to a halt is the strength of the disaster recovery setup. PostgreSQL is built with powerful features that make reliable recovery possible.
This post takes a closer look at how these components work together behind the scenes to protect data integrity, enable consistent restores, and ensure your database can recover from any failure scenario.
This is mostly an architecture-level view, but then again, a lot of HA/DR is about making good architectural decisions.
Leave a CommentWith the recent data center fire and missing government data in South Korea, it felt like a good time to continue with the SQL Server Regrets series of blog posts. We’ll talk about the confusion of thinking you are covered for recoverability – when you just aren’t. This “Regret” comes up far too often when a new client comes in with ongoing backup issues.
Mike gives some real-world and some hypothetical examples around why different teams in a company might be confused around what they’re getting from their SQL Server backups and Mike promotes Jeff Iannucci’s sp_CheckBackup stored procedure as a starting point.
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