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Month: June 2026

In Defense of DirectQuery

Published 2026-06-29 by Kevin Feasel

Chris Webb lays out an argument:

For as long as I’ve been using Power BI – which has been from the beginning – the advice about which storage mode to choose has been the same: use Import mode unless you have a really, really good reason to use DirectQuery mode and even then you’re probably wrong and should use Import mode. Import mode was always a lot faster and a lot easier to tune. Marco’s advice in this LinkedIn post from last year pretty much summed up my attitude and that of every other Power BI expert out there:

Click through for Chris’s argument. As a side note, I saw Conor Cunningham present a session this past weekend at SQL Saturday Austin on the GPU acceleration that Chris mentions in the Fabric Data Warehouse, and under certain circumstances (think “classic Kimball model”), it results in a 2-10x performance improvement, with the expectation somewhere around 3-5x. In other words, a query that takes 50 seconds to run and makes sense to run on GPU drops down to about 10 seconds with no development effort. For well-designed semantic models, this might be a significant factor that pushes the trade-off rubric in the direction of DirectQuery versus Import or DirectLake.

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Invoke-Sqlcmd and Application vs Host Name

Published 2026-06-29 by Kevin Feasel

Greg Low notes a point of confusion:

I’ve always been a fan of having applications identify themselves in their connection strings. It makes tasks like tracing much easier. The tools supplied with SQL Server do a reasonable job of that as you can see above. But many other tools don’t do such a good job.

I was working at a site where they were using Powershell to execute commands for monitoring. I noticed that the Powershell commands did not set the Application Name in the connection string when using Invoke-Sqlcmd.

Read on to learn what Greg’s research discovered.

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Clustering Text via Embeddings and HDBSCAN

Published 2026-06-26 by Kevin Feasel

Ivan Palomaras Carrascosa groups things together:

In this article, you will learn how to build a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based clustering algorithm, to automatically discover topics in unlabeled text data.

Topics we will cover include:

  • How to generate text embeddings for raw documents using a pre-trained sentence-transformers model.
  • How to reduce the dimensionality of those embeddings with UMAP to prepare them for clustering.
  • How to apply HDBSCAN to automatically discover topic clusters and visualize the results.

This is a pretty neat trick that takes advantage of the embedding model’s ability to convert raw text into hundreds (or thousands) of floating point numbers while maintaining enough of the context to differentiate ideas. A lot of it is the original word2vec concepts but scaled up.

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Alerting on Checkpoint Time in Postgres

Published 2026-06-26 by Kevin Feasel

Jeremy Schneider shares some advice:

Checkpoint is the heart of your database. It’s buried deep inside. It’s not something everyone talks about, like well-tuned autovacuum or fast queries. But if checkpointer stops beating, then you’re dead.

In addition to its well-understood job of getting dirty pages written from cache to disk in the background, it also has many smaller jobs that are less widely known. Management of a few shared-memory config settings like sync_standby_names and full_page_writes. Fsync Batching. Deferred file unlinks. Enforcement of archive_timeout.

Click through to see what happens when checkpoint time starts increasing and one important thing you should not do.

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Handling Growth in sysjobhistory

Published 2026-06-26 by Kevin Feasel

Aaron Bertrand has a lot of jobs:

In the first few days of my new role at Infios, we came across an interesting case of memory exhaustion. A whole slew of memory-related error messages would populate the errorlog, then some stack and memory dumps would appear, and then the SQL Server service would just shut itself down without warning. Some of the errors we observed (apologies, it’s a long list, but I want to make sure that any subset might land you here):

Click through to see if you have any of those issues and one possibility of what the cause might be, as well as how to deal with it.

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XML Support in MySQL and Postgres

Published 2026-06-26 by Kevin Feasel

Aisha Bukar lays out how XML works in a pair of relational platforms:

XML (Extensible Markup Language) may no longer dominate modern web APIs the way it once did, but it still plays a critical role in many enterprise systems. Financial institutions, publishing platforms, healthcare systems, government agencies, and large legacy applications continue to rely heavily on XML for structured data exchange and long-term interoperability.

XML also remains deeply embedded in technologies such as SOAP-based APIs, enterprise messaging systems, configuration files, and document-centric workflows where strict structure and validation are essential. This is largely because, unlike lightweight formats such as JSON, XML was designed to handle complex hierarchical documents, namespaces, schemas, and mixed content.

Read on to see how the two open-source relational database platforms handle XML data.

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Optimizing Power BI Data Agents

Published 2026-06-26 by Kevin Feasel

Paul Turley shares some advice:

Amid the AI frenzy, there is a lot of conversation about how business users will use agentic chat to answer business questions rather than interactive, dashboard-style reports. Is there truly a shift in the industry, and is agentic analytics going to change the way most business users consume data?

Just how viable is the whole “chat with your data” option, and is it really a replacement for conventional reporting? I recently heard a VP-level leader at a large consulting firm say something to the effect of “we need to stop investing in dashboard-building skills and focus on creating AI-driven data analysis solutions for our consulting customers.” I’m paraphrasing from memory, but that was the sentiment. Are all business leaders across the industry giving up their dashboards, interactive visual reports and scorecards in exchange for AI chat? No. Of course they aren’t — but conversational analysis is a new way to consume business data.

Much of the advice is very similar to what you’d get for standard dashboard creation, and it makes sense. The clearer your data model is and the tighter your semantic model is, the easier it is for processes to use that semantic model. But Paul also covers some things specific to Data Agents as well.

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Snapshot Testing in R

Published 2026-06-25 by Kevin Feasel

Jakub Sobolewski drills into a particular form of testing:

Snapshot testing is not about screenshots.

Most people meet it through UI regression tests: render a component, save a picture, fail the build when the picture changes. So the technique gets filed away as “the thing that compares images.” That is one use. But not the only one.

The mechanic underneath is general. Capture some output, save it to a file, and on every later run compare fresh output against the saved copy. The output can be a plot. It can also be console text, a log, a data frame, an error message, or a deeply nested list. Anything you can serialize, you can snapshot.

Read on to see how you can perform snapshot testing, using examples in R to demonstrate. H/T R-Bloggers.

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Trusting Outputs from Fabric Data Agents

Published 2026-06-25 by Kevin Feasel

Jens Vestergaard says don’t trust, do verify:

In two previous posts I went down the path of getting a semantic model ready for AI: descriptions on every measure, an instructions file, the schema tidied up enough that a Fabric data agent has something real to read. That work has a satisfying endpoint. The model looks ready.

Ready is not the same as right.

The kind of evaluation Jens is talking about is fundamental to good business intelligence practices, regardless of whether you throw language models into the mix. Where language models do add complexity is the arbitrary scope of questions, how ambiguous people tend to be when writing, and the stochastic nature of answers. All of that makes the problem harder, though at least it isn’t an entirely different class of problem to solve.

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What’s Common in Regular Expressions

Published 2026-06-25 by Kevin Feasel

John Cook muses on regular expression libraries:

The most frustrating aspect of regular expressions is that implementations vary. Features supported in one tool may not be supported at all in another tool, or they may be supported with slightly different syntax.

I learned regular expressions in the context Perl, a maximalist regex environment. This led to frustration when features I expect to work are missing [1]. One way around this is to use Perl analogs of other tools, but this is very non-standard. I want to be able to send colleagues and clients code that works out of the box.

Click through for some thoughts about the lowest common denominator for what products tend to support around regex. This is one of several tricky things when working with regular expressions: you may know a great way to solve a specific class of problem, but does the particular engine you’re using actually support that method?

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