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Curated SQL Posts

Session-Scoped Temp Tables in Microsoft Fabric now GA

Published 2025-04-23 by Kevin Feasel

Twinkle Cyril gets something GA:

Introducing distributed session-scoped temporary (#temp) tables in Fabric Data Warehouse and Fabric Lakehouse SQL Endpoints.

#temp tables have been a feature of Microsoft SQL Server (and other database systems) for many years. In the current implementation of Fabric data warehouse, #temp tables are session scoped or local temp tables. Global temp tables are not included in this release.

Session-scoped #temp tables exist only within the session in which they are created and last only for the duration of that session. They are not visible to other users or sessions and are automatically dropped from the system once the session ends or the user decides to drop the temp table. These tables are accessible to all users without requiring specific artifact-level permission.

Click through for examples of how it works and how you can specify a session-level temp table over a local temp table.

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Expression Reordering in PostgreSQL

Published 2025-04-23 by Kevin Feasel

Andrei Lepikhov speeds up a query:

Occasionally, you may come across queries featuring complex filters similar to the following:

SELECT * FROM table
WHERE
  date > min_date AND
  date < now() - interval '1 day' AND
  value IN Subplan AND
  id = 42';

And in practice, it happens that a simple rearrangement of the order of conditions in such an expression allows for speeding up (sometimes quite notably) the query execution time. Why?

Read on for the answer. In a perfect world, SQL is a 4th generation language and the order of operations should make zero difference for query performance. In practice, as Andrei shows, this is a challenge for the developers of the relational databases we use.

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Restoring a Single Data Page in SQL Server

Published 2025-04-23 by Kevin Feasel

Stephen Planck turns the page:

Most of the time, corruption in SQL Server is either nonexistent or so widespread that you have no choice but to perform a file or full‑database restore. Yet an awkward middle ground exists: a handful of pages—perhaps only one—become unreadable while the rest of the database remains perfectly healthy. A full restore would repair the damage, but at the cost of rolling back hours of work and locking users out of an otherwise functional system.

That is precisely why Microsoft built RESTORE … PAGE. When you meet a short list of prerequisites (FULL or BULK_LOGGED recovery model, an unbroken backup chain, and a page that is not allocation metadata), you can surgically overwrite just the bad 8‑KB chunks, roll them forward with transaction‑log backups, and return the database to service in minutes rather than hours.

Read on to see how it all works, as well as situations in which this isn’t the right answer.

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Pitfalls in Software Testing

Published 2025-04-22 by Kevin Feasel

Ngọc lê builds a list:

Software testing plays a crucial role in ensuring the delivery of high-quality products. However, even experienced testers can fall into common traps that compromise the effectiveness of testing processes, allowing defects to slip into production. Avoiding these pitfalls is essential for maintaining the reliability and functionality of software. In this blog, we’ll explore some of the most common software testing mistakes and provide strategies to overcome them.

This is specifically for software testing, but most of these principles apply the same for database work.

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Comparing Microsoft Fabric to Snowflake

Published 2025-04-22 by Kevin Feasel

Evanjalin Joseph lays out a comparison:

Take ShopSmart, a global retail chain that operates both online and offline. The company wants to combine its sales, inventory, and customer data in order to facilitate real-time reporting and predictive analytics. Two top platforms are being assessed by the IT team for this change.

Azure, Power BI, and Microsoft 365 are already widely used by ShopSmart, which is in line with Fabric’s integrated ecosystem. The alternative, however, provides more multi-cloud flexibility and strong performance on structured data. The group has to choose between selecting a more specialized warehousing solution with more deployment options or making use of its current Microsoft investments.

Let’s examine the differences between the two platforms.

Click through for an overview of each platform and how they stack up against one another.

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Digging into the Microsoft Fabric Monitoring Hub

Published 2025-04-22 by Kevin Feasel

Reitse Eskens shows off Microsoft Fabric’s Monitor hub:

When we create a data solution for our company or clients, we create the best, right? Sure, we do. But still, monitoring the solution is something you should never forget. Your solution can be stable, but the data usually isn’t. And it just feels good to know all the data has been processed correctly.

So, let’s dig into monitoring Microsoft Fabric.

Read on to see how it works, as well as plenty of thoughts, advice, and critique from Reitse.

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The CHOOSE Function in SQL Server

Published 2025-04-22 by Kevin Feasel

Louis Davidson chooses the form of our destroyer:

I preface a lot of what I write with whether or not it is for a “practical” use. One of the Simple Talk authors used this function in a forthcoming article. and I realized I hadn’t heard of it before (or I forgot about it… which is not completely unlikely.) The practical use was to generate some data and have at least a little variety to the values.

The CHOOSE function has this syntax.

CHOOSE(item_to_choose, item1, item2 [,item3]…[itemN])

In fairness to Louis, CHOOSE() was one of those additions to SQL Server 2012 that we mostly forgot about. It’s similar in that vein to IIF() if you weren’t living in Excel at the time. In any event, Louis takes CHOOSE() through its paces, showing some useful scenarios and stretching the limits to see what happens.

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Dynamic Pareto Analysis in Power BI

Published 2025-04-22 by Kevin Feasel

Marco Russo and Alberto Ferrari get us to 80%:

The Pareto analysis is an analytical technique used to identify the most impactful elements within a dataset, based on the principle that a small proportion of causes often leads to a large proportion of effects. For example, the ABC Classification in DAX Patterns is also based on the Pareto principle. However, typical implementations often face limitations. The analysis based on the Pareto principle commonly uses categorical axes, such as customer names or identifiers, making it impossible to leverage a continuous axis on a Power BI line chart. A categorical axis creates a scrollbar on the line chart when there are too many data points, limiting the ability to compare the distribution of data points in different categories within the same line chart.

This article shows how to overcome this limitation by introducing a numeric axis reflecting each item’s position based on the selected measure.

Read on to see how it all works.

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