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Category: Microsoft Fabric

No More Default Semantic Models in Microsoft Fabric

Published 2025-09-09 by Kevin Feasel

Nicky van Vroenhoven has good news for us:

Another quick post, because today is an important day for everyone working with Fabric and Power BI!

Last month, Microsoft announced they are Sunsetting Default Semantic Models: Yaay! 
Today marks that day: No more automatic child semantic models!

The idea of having a default semantic model seemed like a good one, but the problem was that too many environments had very specific needs that a default semantic model couldn’t anticipate or address. As a result, these tended to confuse end users more than save them time.

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The Consequences of Hitting Semantic Model Guardrails

Published 2025-09-08 by Kevin Feasel

Chris Webb smashes into a wall:

Direct Lake mode in Power BI allows you to build semantic models on very large volumes of data, but because it is still an in-memory database engine there are limits on how much data it can work with. As a result it has rules – called guardrails – that it uses to check whether you are trying to build a semantic model that is too large. But what happens when you hit those guardrails? This week one of my colleagues, Gaurav Agarwal, showed me the results of some tests that he did which I thought I would share here.

Click through to see what happens when you go past one of those guardrails.

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What’s New in Microsoft Fabric Data Warehouse

Published 2025-09-04 by Kevin Feasel

Sowmya Sivaraman has an update:

Welcome to the August 2025 edition of What’s New in Fabric Warehouse. As summer winds down, despite August being a slower month, our team continued to deliver meaningful updates. We shipped several new features focused on enhancing data ingestion, improving the data management, and streamlining security. At the same time, much of our energy is going into preparing exciting announcements for FabCon Vienna — stay tuned for what’s coming next. Whether you’re optimizing workloads, building with SQL, or exploring new integrations, this roundup highlights improvements we think you’ll find valuable.

Click through for a list of changes.

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Comparing Microsoft Fabric Consumption for Notebooks and Warehouse SQL Queries

Published 2025-09-03 by Kevin Feasel

Gilbert Quevauvilliers performs a comparison:

I saw that there was an update where it is now possible to use the Microsoft Fabric Warehouse to copy data directly from OneLake into the Warehouse.

This got me thinking, which would consume more capacity to get the data into the Warehouse table. As well as which one would be faster.

To do this I am going to be running a SQL query in the Warehouse.

Next, I will use a Notebook to copy the data from the OneLake files section to a Warehouse table.

Gilbert’s specific query involves loading data from a variety of CSV files into a lakehouse via notebook, and then into a warehouse table. Read on for the results.

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Dataflows Gen2 Tips and Tricks

Published 2025-09-03 by Kevin Feasel

Jon Vöge provides advice on the least beloved ELT process:

Dataflows Gen2 are frequently (and often rightfully so) bashed for their performance inefficiencies. Especially in comparison with other ingestion and transformation tools in Fabric (Notebooks, Pipelines, Copy Jobs, SPROCs).

The fact remains however, that in the hands of a self-service developer, they are an incredibly powerful tool – if you can spare the compute on your capacity.

In this article, I will highlight tips and tricks to make the most of working with Dataflow Gen2 in Fabric. The list is by no means exhaustive, but simply consists of a bunch of tips which I found useful in the past year, including new and overlooked features, as well as old best practices:

Read on for some things that are new to Dataflows Gen2, working with SharePoint, and making data loads not quite as slow.

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Worst-Case Testing for Direct Lake Semantic Models

Published 2025-09-02 by Kevin Feasel

Chris Webb updates a prior post:

Two years ago I wrote a detailed post on how to do performance testing for Direct Lake semantic models. In that post I talked about how important it is to run worst-case scenario tests to see how your model performs when there is no model data present in memory, and how it was possible to clear all the data held in memory by doing a full refresh of the semantic model. Recently, however, a long-awaited performance improvement for Direct Lake has been released which means a full semantic model refresh may no longer page all data out of memory – which is great, but which also makes running performance tests a bit more complicated.

Read on to learn more about the improvement as well as how you can still perform your performance testing.

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August 2025 Microsoft Fabric Feature Summary

Published 2025-08-28 by Kevin Feasel

Patrick LeBlanc has a list:

The August 2025 Fabric Feature Summary showcases several exciting updates designed to streamline workflows and enhance platform capabilities. Notably, users will benefit from the new flat list view in Deployment pipelines, making navigation and management more intuitive. In addition, expanded support for service principals and cross-tenant integration with Azure DevOps reflects Microsoft’s commitment to versatile and secure enterprise solutions.

As usual, there’s a lot on the list to digest, but Patrick does a great job of laying out the content in an accessible manner.

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Materializing Lake Views in Microsoft Fabric

Published 2025-08-27 by Kevin Feasel

Sairam Yeturi reduces ETL and ELT requirements:

Organizations often face challenges when trying to scale analytics across large volumes of data stored in centralized SQL databases. As business teams demand faster, more tailored insights, traditional reporting pipelines can become bottlenecks. By adopting Lakehouse architecture with Microsoft Fabric, business groups can mirror their SQL data into OneLake and organize it using the Medallion architecture—Bronze, Silver, and Gold layers. Materialized lake views play a crucial role in this setup, enabling automated, declarative transformations that clean and enrich data in the Silver layer. This empowers teams to build reliable dashboards and AI-driven insights on top of curated data, all while maintaining performance, governance, and security on a scale.

In this post, we will cover how enterprises can use materialized lake views to streamline data orchestration and enhance data quality, monitoring across silver and gold layers, while mirroring their SQL DB tables to Fabric in the Bronze layer.

The best use case for this is a scenario in which your underlying data is already essentially in a star schema or at least easily transformable into one, and you have no interest in modifying the data in the view directly. Do read the limitations before digging in, though, as there are some big ones.

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Passing Selections from Visuals to Translytical Task Flows

Published 2025-08-26 by Kevin Feasel

Jon Vöge sends along some data:

A common misconception about Translytical Task Flows is that the only way for you to parameterize and pass user inputs to the User Data Function, is through Slicers in Power BI.

That is not true at all.

In fact, one of the most powerful ways of integrating Task Flows into your Power BI reports, is by allowing user selections made in visualisations in your report, flow through to your task flow.

Read on to see how you can do this.

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Microsoft Fabric Pipeline Copy Job Activity in Preview

Published 2025-08-26 by Kevin Feasel

Connie Xu makes an announcement:

We’re thrilled to announce that the Copy job Activity is now in Preview! 

This new orchestration activity brings the simplicity of the Copy job item directly into your Microsoft Fabric Data Factory pipelines, enabling you to manage data movement alongside transformations, notifications, and more; all in one place. 

Read on to learn more about it, including how it differs from the Copy activity and the Copy job item.

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