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Curated SQL Posts

Generative AI Assistance in Building Power BI Custom Visuals

Published 2025-03-25 by Kevin Feasel

Kurt Buhler discusses a process:

In Power BI, advanced report creators often need to use custom visuals to fulfill their requirements or create certain designs. In previous articles that we published at SQLBI, we discussed the options available to make custom visuals, such as SVG visuals that you can make by using DAX. We also gave examples of when you might choose one approach over another, for example, if you want to make a bullet chart. However, creating custom visuals in Power BI is complex, and requires technical skills that most Power BI report creators do not have. In this article, we examine how you can use AI assistance to help you plan and create custom visuals.

A high-level overview of the process we will take and the desired result is below. It is important to emphasize that this article focuses on the general process, and not specific steps to obtain the result.

Click through for a long-form article on the subject. I’m generally fairly sour on relying too much on generative AI solutions for, well, much of anything. That’s part of why you see so few posts on the topic here. My main problem is that it works best in situations where you already know enough to separate wheat from chaff, or good code from broken/insecure/buggy code. I think Kurt strikes a good tone in this article and it’s well worth the read.

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Finding Why a SQL Server Plan Failed to Go Parallel

Published 2025-03-25 by Kevin Feasel

Grant Fritchey looks for an answer:

Let’s face it, most of the time, you probably don’t want your SQL Server execution plans to go parallel. After all, that’s why, instead of adjusting the Cost Threshold for Parallelism (as you should have), you’ve set the Max Degree of Parallelism to 1 (I see you out there). However, some of you recognize that, in fact, some queries need to run in Parallel.

Yet, sometimes, a query you think should run in parallel doesn’t. Can you tell why a plan didn’t go parallel?

Read on to learn how.

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Low-Code Tradeoffs and an Example of SFTP

Published 2025-03-25 by Kevin Feasel

Meagan Longoria talks trade-offs:

Low-code solutions often accelerate development and make tasks accessible to people who can’t or don’t want to write their own code. But it’s important to remember that it’s a trade-off. You are often trading decreased development and maintenance time for limited configuration options and minimal monitoring capabilities. Low-code solutions are great…until they aren’t.

This kind of thing can pop up in code-based libraries or APIs as well, though there’s typically another viable solution. With low-code solutions, veering from the happy path often lands you in a world of frustration.

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Performance Profiles of SUM() and SUMX() in DAX

Published 2025-03-24 by Kevin Feasel

Eugene Meidinger blows the doors off of this conspiracy:

For years, I told people to avoid iterators. I compared them to cursors in SQL, which are really bad, or for loops in C# which are normally fine. I knew that DAX was column based and that it often broke down when doing row-based operations, but I couldn’t tell you why.

Advice to avoid iterators is often based on a misunderstanding and a misapprehension of how the Vertipaq engine works. If you are blindly giving this advice out, like I was, you are promoting a fundamental misunderstanding of how DAX works.

Eugene has since opened his eyes and can see through the lies.

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The Design of DataDiluvium

Published 2025-03-24 by Kevin Feasel

Adron Hall wraps up a series on DataDiluvium. First up is some commentary on data generation:

In Parts 1 and 2, I set up the development environment and implemented the schema parsing functionality. Now, I’ll explore the data generation system and final implementation details that make DataDiluvium a complete solution.

And finally we have some additional notes and wrapup:

In my previous three posts, I covered the core functionality of DataDiluvium. In this follow-up post, I’ll explore the additional features, utilities, and implementation details that I’ve added to enhance the application’s functionality and developer experience.

Check out both posts. I do enjoy seeing people walk through and explain some of the key concepts and decisions they’ve made when developing solutions.

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Speeding up Dataflow Validation and Publish Times

Published 2025-03-24 by Kevin Feasel

Chris Webb doesn’t want to wait:

If you’re working with slow data sources in Power BI/Fabric dataflows then you’re probably aware that validation (for Gen1 dataflows) or publishing (for Gen2 dataflows) them can sometimes take a long time. If you’re working with very slow data sources then you may run into the 10 minute timeout on validation/publishing that is documented here. For a Gen1 dataflow you’ll see the following error message if you try to save your dataflow and validation takes more than 10 minutes:

Click through for that common error message, as well as some tips to avoid this issue. There was also an interesting approach in the comments section that circumvented the problem as well.

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Full-Text Search Modes in MySQL

Published 2025-03-24 by Kevin Feasel

Chad Callihan performs a text search:

When it comes to MySQL queries against columns containing text, FULLTEXT indexes offer a variety of approaches that go beyond your basic pattern matching. When creating a FULLTEXT index for querying text data, we can use MATCH and AGAINST along with our choice of three modes.

Click through for the three modes, as well as examples of queries for each.

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Virtualizing Hadoop Data into OneLake via Apache Ozone

Published 2025-03-21 by Kevin Feasel

James Morantus hits us with a blast from the past:

Microsoft Fabric OneLake shortcuts facilitate the virtualization of data from various cloud object stores and on-premises environments. For on-premises sources like Cloudera/Apache Ozone, the OneLake S3 Compatible Shortcut can be utilized to connect to these data sources. With OneLake Shortcuts, users can create a virtual reference to their Cloudera cluster data without moving or duplicating the data. To learn more about Fabric OneLake shortcuts, reference this blog OneLake with shortcuts.

If this was a decade ago, I’d be a lot more excited. But it is kind of wild how quickly the data landscape changed, with the adoption of Spark over classic Hadoop; cloud-based data lakes over HDFS; and more focused dataset sizes over “give me everything.”

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