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Month: April 2026

Defining the Fabric Ontology

Published 2026-04-29 by Kevin Feasel

Mike Donnelly explains a term:

The short version: Fabric Ontology is the semantic backbone of Microsoft Fabric. It’s the layer that defines what your business data actually means.

If you’ve ever worked in a large organization, you know the problem. One team calls them “Customers,” another calls them “Clients,” and a third calls them “Account Holders.” Without a shared meaning, your analytics become a mess of conflicting vocabularies. An ontology is just a structured way of naming things and describing their relationships so everyone—and every tool—is using the same dictionary.

I think this is all correct, but I think there’s something more to ontologies than that. At least in the Palantir world, the ontology is not just the business definitions and concepts, but it’s also the actions you can take against that data. In other words, you might have Customers, Clients, and Account Holders. You can add a new customer, update the customer details, send a welcome to a new account holder, etc. Each of these actions is baked into the ontology as well. That’s what makes it different from simply defining where the data lives and how we got it in its current shape.

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Support for Typst in knitr

Published 2026-04-28 by Kevin Feasel

Yihui Xie makes an announcement:

A few weeks ago I added preliminary support for Typst to knitr. The way it works is simple: if your file has the extension .Rtyp, knitr will recognize it as a Typst document, knit it, and produce a .typ output file. The chunk syntax follows the same Markdown-style fenced code block convention: ```{r} to start a chunk and ``` to end it, with inline R expressions written as `r expr`. A minimal example (hello.Rtyp):

Click through for that example, as well as some notes on Typst and HTML.

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Tips for Using Bar Charts

Published 2026-04-28 by Kevin Feasel

Ruben Van de Voorde shares some tips:

The bar chart is one of the oldest statistical graphics we have. William Playfair published the first bar chart for categorical comparison in 1786: a horizontal bar chart of Scotland’s imports and exports with trading partners. Two and a half centuries later, it’s a familiar sight wherever numbers are shown visually: news stories, research reports, business dashboards; bar charts are everywhere. Most people have seen one before and instinctively know how to read them.

Click through for plenty of examples of where bar charts work best, as well as important notes when using them. I’m particularly fond of Cleveland dot plots versus bar charts, but a good bar chart does tell an important story.

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Unmaterialized Columns in Power BI

Published 2026-04-28 by Kevin Feasel

Teo Lachev has ideal columns:

Coming back from a long vacation, I’ve almost missed this interesting Power BI enhancement: Power BI unmaterialized calculated columns. Normally, I avoid the traditional DAX calculated columns for a variety of reasons, such as confusion about where business logic is applied, limited support across storage modes (for example, Direct Lake doesn’t support them), longer refresh times, etc. This not to say that calculated columns can’t be useful, such as in the case where you need to flatten a parent-child hierarchy. But unmaterialized calculated columns could open interesting scenarios that go beyond content translation to other languages mentioned by Microsoft in the April 2026 update.

Click through to learn what unmaterialized columns are and how they work.

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Time Delay for Online Checksums in PostgreSQL

Published 2026-04-28 by Kevin Feasel

Cristophe Pettus notes an upcoming change to PostgreSQL 19:

For about fifteen years the answer to “can I turn on data checksums without an initdb?” has been “not really.” pg_checksums showed up in PostgreSQL 12 and made the job survivable, but you still had to shut the cluster down. For anyone running 24×7 production, that has left the same three options: take the downtime, fail over through a checksummed replica, or live without checksums.

PostgreSQL 19 adds a fourth path. A commit from Daniel Gustafsson on April 3rd wires up online enabling and disabling of data checksums: the command completes immediately, and the cluster keeps serving traffic while a background process rewrites every heap and index page in the cluster to carry (or drop) the checksum.

Read on to see what it will do, as well as the consequences.

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Storage Load Testing SQL Server

Published 2026-04-28 by Kevin Feasel

Anthony Nocentino designs a test:

I’ve been doing storage load tests for SQL Server for a long time, both as a consultant and now in my work at Everpure, and I see the same patterns over and over. Someone spins up a VM with two vCPUs, points it at a storage subsystem (cloud or on-prem), runs a thousand threads at it, and then concludes that the storage stinks. Or the opposite, where they buy a 64 gigabit HBA, plug it into the wrong PCIe slot, and wonder why they’re leaving half of the capacity on the table.

Designing a good load test isn’t about pushing the biggest number you can find. It’s about knowing what your gear is supposed to do, driving load until you find the point where the system breaks down, and measuring at the right places along the way. In this post, I’m going to walk you through how I think about designing a storage load test, the tools I reach for, and the pitfalls that will invalidate your results if you’re not paying attention.

Click through for Anthony’s thought process.

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Building Materialized Lake Views in Microsoft Fabric

Published 2026-04-27 by Kevin Feasel

Nikola Ilic presses the Easy button:

For the longest time, building a medallion architecture in Microsoft Fabric meant stitching together a small orchestra of moving parts: notebooks for the transformations, pipelines for orchestration, schedules for refresh, custom code for data quality checks, and the Monitor Hub for keeping an eye on whether anything actually worked. Every layer worked – until something didn’t, and then you had to figure out which layer broke, why, and which downstream layers got affected along the way.

If you’ve ever tried to debug a silver layer that didn’t update because the bronze notebook failed three hours ago, you know exactly what I’m talking about.

Then, at FabCon Atlanta in March 2026, materialized lake views (MLVs) went generally available. And the story they’re telling is simple: what if your entire medallion pipeline could be a few SELECT statements?

Let me walk you through the whole thing – what they are, how they work, what changed between preview and GA, and where they fit (and where they don’t) in your architecture.

Read on for that walkthrough.

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Generating Sample Data in Fabric Dataflows

Published 2026-04-27 by Kevin Feasel

Chris Webb builds some data:

Back in December the FabricAI.Prompt() M function was released in Fabric Dataflows Gen2. Most of the people writing about it at that time, as in this great post by my colleague Sandeep Pawar, focused on calling this function for each row in a table – something that the UI in the editor makes easy. However the FabricAI.Prompt() function itself is a lot more flexible. You can use it to summarise whole tables of data as I showed here; you can also use it to generate sample data. This is similar to what I blogged about here where I got Copilot to generate M code that returned sample data but using FabricAI.Prompt() is maybe a bit simpler.

Click through to see how.

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