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Category: Microsoft Fabric

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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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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Cross-Workspace MLflow Logging Available in Microsoft Fabric

Published 2026-04-24 by Kevin Feasel

Ruixin Xu announces a feature now generally available:

Cross-workspace logging works through the synapseml-mlflow package, which provides a Fabric-compatible MLflow tracking plugin. The core idea is simple: set the MLFLOW_TRACKING_URI* to point at your target workspace and use standard MLflow commands. Your experiments, metrics, parameters, and registered models land in the workspace you choose — not just the one you’re running in.

Read on for the full announcement.

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Using Change Event Streaming for Microsoft Fabric Real-Time Analytics

Published 2026-04-23 by Kevin Feasel

Xu Jiang and Nikola Zagorac take a look at Change Event Streaming:

Traditionally, Change Data Capture (CDC) has been the go-to mechanism for tracking SQL Server data changes. However, CDC relies on polling-based capture with intermediate change tables, introducing latency and operational overhead, such as managing polling, offsets, and replaying windows in connector. Change Event Streaming (CES), introduced in SQL Server 2025, Azure SQL Database, and Azure SQL Managed Instance, takes a fundamentally different approach: it pushes data change events directly from the database engine to external streaming platforms in real time. Built on the CloudEvents specification, CES delivers structured JSON messages with the operation type and full row data – eliminating intermediate tables and reducing end-to-end latency to near zero.

Click through for more information, though Change Event Streaming is still officially a preview feature in SQL Server 2025

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Microsoft Fabric Eventstream Network Security Features

Published 2026-04-22 by Kevin Feasel

Alex Lin looks at network security features:

Eventstream in Fabric Real-Time Intelligence stream data from both inside and outside the Fabric platform. When your external sources sit behind firewalls or in private networks, choosing the right network security feature is essential. This post breaks down the available options in Eventstream and helps you determine which one fits your scenario.

Click through for more information.

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Clustering in Fabric Warehouse

Published 2026-04-21 by Kevin Feasel

Koen Verbeeck speeds things up:

We are building a large warehouse in Microsoft Fabric using the warehouse. Our biggest fact tables have some performance issues when we are running our analytical queries, and it seems we cannot use indexes in the Fabric Warehouse. Is there some way to improve performance?

Click through to see how you can use clustering to improve the performance of warehousing queries, as well as some of the pre-requisites to make it work.

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Choosing Names in Microsoft Fabric

Published 2026-04-20 by Kevin Feasel

Nikola Ilic asks, what’s in a name?

My dear Microsoft Fabric friends – if you’ve ever opened a workspace and seen “Lakehouse”, “Lakehouse 1”, “lh_test_v2”, and “NewLakehouse_DELETE_ME” all sitting next to each other, this post is for you

Three weeks into a fresh Fabric tenant, things look great. Twelve weeks in, you’re staring at 47 workspaces, three of them called something like “Test – DO NOT USE”, and nobody on the team can remember which Lakehouse holds the actual production sales data.

I don’t know how Nikola has figured out my naming strategy so well. Click through for a systematic attempt to standardize naming for Fabric objects.

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Validating DAX against a Lakehouse via Semantic Link

Published 2026-04-17 by Kevin Feasel

Jens Vestergaard performs some checks:

A semantic model is a promise. It promises that the numbers in your reports match the data in your lakehouse. But after enough model changes, renamed columns, new relationships, and tweaked measures, that promise gets harder to verify. I wanted a way to check it programmatically.

This is my second submission to the Fabric Semantic Link Developer Experience Challenge. The first was a DAX unit test harness that compares measures against hardcoded expected values. That works well for known business rules, but it has a limitation: someone has to decide and maintain what the “right” answer is. For a model with hundreds of measures across dozens of filter contexts, that does not scale.

Click through to see what Jens did instead.

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