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Curated SQL Posts

The Good and Bad of Microsoft Fabric Variable Libraries

Jon Lunn digs in:

One of the big issues with Deployment Pipelines in Fabric, or as I call them Disappointment Pipelines, has been the lack of being able to parameterise connections. You do have deployment rules in the pipelines, but they are limited in functionality and don’t support pipeline parameters (boo!), so if you need to push and change items between workspaces in a typical Development, Test and Production workspaces scenario, you had to configure the connections manually, which is a massive pain. Variable Libraries should make the experience of deployment a lot easier.

Read on to see how they work, as well as some of the existing pain points around them.

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Data Replication and Columnstore

Niko Neugebauer continues a series on columnstore:

In the Columnstore Indexes space, there is a long-standing “tradition” in Microsoft to ignore the needs of the customers for data replication. It has started with with the original SQL Server 2012 release not supporting any data manipulation operations besides Partition switching. Since then it has been improved from version to version up until SQL Server 2016 where Nonclustered Columnstore Indexes has received a support for the Transactional Replication, and voila – that’s where it has stopped!

Read on for the frustration involved in moving around columnstore data.

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Workspace Identity Authentication in Power BI

Teo Lachev looks at a new way of authenticating:

What credentials do you use to refresh your Power BI semantic models from Azure SQL SKUs, such as Azure SQL Database. Probably your credentials or a designated Entra account? Both are not ideal for a variety of reasons, including requiring a password. More advanced users might be using service principals, which are more secure but require secret renewal after a maximum of 24 months, which is a hustle.

Read on to learn about a new alternative in workspace identities.

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Interesting Data is Usually Wrong

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.

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Regular Expression Functions in SQL Server 2025

Tomaz 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.

And Day 9 hits two more:

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.

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Page Compression on Heaps

Vlad 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.

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Text Search in PostgreSQL

Jay 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.

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OneLake Security ReadWrite Access

Kiefer 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.

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Tracking Historical Changes in Microsoft Fabric

Kenneth 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.

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