A Fine Slice Of SQL Server
Mike Cisneros breaks the bad news:
Tony Twyman made his name as a pioneer in the field of audience research for television and radio in the UK. For our discussion today, though, he’s best remembered for a single, enduring quotation, which is now known as Twyman’s Law:
“Any figure that looks interesting or different is usually wrong.”
Read on for a good example of how the hunt for an interesting story turned into something resolutely normal after fixing a pair of data issues.
Leave a CommentTomaz Kastrun continues an advent of SQL Server 2025. Day 8 looks at a pair of regular expression-related functions:
Continuing with SQL Server 2025 T-SQL functions for Regular Expressions for in string and count functionalities.
Last two functions in the family of new T-SQL functions that were shipped with RegEx, are REGEXP_MATCHES() and REGEXP_SPLIT_TO_TABLE().
Read on to see how all four of these work.
Leave a CommentVlad Drumea explains why page compression might not give you quite what you expect:
I recently ran into SQL Server’s page compression being applied to a heap, and I figured I’d cover why that won’t work how some folks expect.
SQL Server’s page compression is really neat when applied on tables and indexes that are good candidates for it.
Even more so in cloud environments where storage costs can quickly add up.
Honestly, this is just a good reason to push for clustered indexes on all tables in SQL Server. I’ll call it good reason 5 out of 12.
Leave a CommentJay Miller is looking for strings in all the wrong places:
I like to think of this like seeing a doctor. You can go to a family doctor and they can help you with most things. For specific results, it’s better to see a specialist who has a better understanding of the particular issue.
Search is the same way. At the end of the day, you get results but what you put into your search will affect what you get. That said there are some search methods that work better than others depending on your data.
Read on for several techniques that are available. I do think the headers denigrate LIKE/iLIKE a bit too much, as it works pretty well in many circumstances. But there are definitely good times to bring out the more powerful mechanisms, as this article shows.
Leave a CommentKiefer Sheldon practices least privilege:
Many data teams face the same challenge: balancing the need for open collaboration with the responsibility of protecting sensitive information. As organizations grow, data often lives across multiple domains—some containing critical or confidential datasets—while partner teams may only need access to a subset of that information.
Until recently, maintaining this balance often meant trade-offs. Teams had to choose between a fragmented storage setup or overexposing data just to keep their workflows running smoothly.
Read on to see how this works.
Leave a CommentKenneth Omorodion generates a snapshot:
In most modern businesses, by default, operational systems are managed in a way that only shows the current view of things in their data like active tickets, open incidents, active complaints, and daily sales. While this is a great way to monitor day-to-day reporting, it however tends to hide an important narrative for the business. For instance, it does not show how things have changed over time. It also does not tell a story on how previous periods compared to the current, in terms of the actual state of the data.
So, without a snapshot view implementation, there is no way to accurately view when data changes, and this may lead to a loss of the previous view forever with no way to retrieve that snapshot.
Click through to see how.
Leave a CommentHans-Jürgen Schönig gives us a list:
This document explains which options are available and which problems one can solve with PostgreSQL. We have listed the most common setups and some of the most common recommendations.
Read on for three common scenarios.
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