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Category: Microsoft Fabric

Pain Points around Direct Lake

Published 2026-02-27 by Kevin Feasel

Teo Lachev describes a pair of problems:

I’m helping an enterprise client modernize their data analytics estate. As a part of this exercise, a SSAS Multidimensional financial cube must be converted to a Power BI semantic model. The challenge is that business users ask for almost real-time BI during the forecasting period, where a change in the source forecasting system must be quickly propagated to the reporting the layer, so the users don’t sit around waiting to analyze the impact. An important part of this architecture is the Fabric Direct Lake storage to eliminate the refresh latency, but it came up with a couple of gotchas.

Click through for those two problems.

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Building Power BI Reports from the Desktop or Fabric

Published 2026-02-27 by Kevin Feasel

James Serra clears up some confusion:

If you’re a Power BI report author who’s just getting into Microsoft Fabric, you’ve probably asked the same question I hear over and over: am I supposed to stop using Power BI Desktop now?

It’s a fair question. Power BI Desktop is a Windows app that has traditionally been the place where report authors do everything: get data, transform it, model it, and build the report. Microsoft even describes that “connect, shape/transform, then load” experience as part of how Power BI Desktop works with Power Query.

Fabric changes the feel of that workflow because Power BI is now also a first-class experience in the browser inside the Fabric portal. And that browser experience isn’t just “view and share” anymore. You can edit semantic models in the service, including using Power Query for import models and building reports directly from that same environment.

Read on to see, for a brand new report, which of the two models can make the most sense.

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An Overview of the Fabric Native Execution Engine

Published 2026-02-27 by Kevin Feasel

Ankita Victor-Levi introduces a new processing model:

In today’s data landscape, as organizations scale their analytical workloads, the demand for faster, more cost-efficient computation continues to rise. Apache Spark has long been the backbone of largescale data processing with its in‑memory processing and powerful APIs, but today’s workloads demand even better performance.

Microsoft Fabric addresses this challenge with the Native Execution Engine—a vectorized, C++ powered execution layer that accelerates Spark jobs with no code changes, reduced runtime, and at no additional compute cost. This blog post will take you behind the scenes to give an overview of how the engine works and how it delivers performance gains while preserving the familiar Spark developer experience users already know and love.

Read on to learn more about its capabilities and current limitations.

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Microsoft Fabric Updates for February 2026

Published 2026-02-26 by Kevin Feasel

Katie Murray puts together a list:

This month brings a wide range of enhancements across the Fabric platform—from improvements to the OneLake Catalog and developer experiences, to meaningful updates in Data Engineering, Data Factory, Real‑Time Intelligence, and more. Whether you’re building, operating, or scaling solutions in Fabric, there’s plenty here to explore. And with FabCon just weeks away, February’s updates are a great preview of what’s ahead.

Click through for a big list, though I’d expect the March list to be significantly larger.

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Workspace Operations with MicrosoftFabricMgmt

Published 2026-02-26 by Kevin Feasel

Rob Sewell continues a series:

The workspace is the fundamental unit of organisation in Microsoft Fabric. Everything lives inside a workspace — your lakehouses, warehouses, notebooks, pipelines, reports. Managing workspaces is therefore the first practical skill to build, and MicrosoftFabricMgmt makes it straightforward.

Read on for examples covering how to create, update, list, retrieve, or remove workspaces.

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Comparing Lakehouse and Warehouse Performance again

Published 2026-02-25 by Kevin Feasel

Gilbert Quevauvilliers provides some more comparisons:

I learnt a lot and based on the feedback people asked for me to compare the Lakehouse vs the Warehouse with 1 billion rows.

What I also did this time was to optimize anything I could with regards to loading data into the Lakehouse or the Warehouse based on the feedback I received.

Below is a list of the changes I made

Read on for those changes and how they affected performance. That’s the tricky part about performance comparisons: unless you know how to tweak all options equally, you can end up with skewed results.

I’d also be interested in how the Eventhouse fares. I believe that, when it comes to data retrieval, the Eventhouse is the fastest option available to us.

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An Introduction to MicrosoftFabricMgmt

Published 2026-02-25 by Kevin Feasel

Rob Sewell has a series of posts on MicrosoftFabricMgmt. The first post provides an introduction:

I have been introducing the Microsoft fabric-toolbox — covering the toolbox itself, FUAM, and FCA. All excellent tools. But there is one item in the toolbox that I have been personally involved in building, and it is the one I am most excited to write about.

Today I am kicking off a series of posts about MicrosoftFabricMgmt — an enterprise-grade PowerShell module that gives you comprehensive, scriptable control over the entire Microsoft Fabric REST API. It is hosted as part of the fabric-toolbox on GitHub.

The second post covers installation and authentication:

Yesterday I introduced the MicrosoftFabricMgmt module and explained what it can do. Today we are getting hands on — installing the module, sorting out dependencies, and making your first connection to Microsoft Fabric.

By the end of this post you will have the module installed, be authenticated, and have your first list of Fabric workspaces in your terminal.

The third post involves not having to deal with a bunch of GUIDs:

Which workspace is 948d3445-54a5-4c2a-85e7-2c3d30933992? Which capacity? Who knows — go look it up. Multiply that by fifty items across ten workspaces and you have a frustrating afternoon ahead of you.

The PowerShell Module**MicrosoftFabricMgmt** solves some of this frustration.

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Working with Recent Data in Dataflows Gen2

Published 2026-02-24 by Kevin Feasel

Penny Zhou sees recent datasets:

How much time do you spend navigating to the same data sources when building dataflows? Data preparation is an iterative process—you often return to the same sources as you refine your dataflows, add new transformations, or create similar workflows. If you find yourself repeatedly connecting to the same tables, files, or databases, the Recent data module in Dataflow Gen2 is designed for you. This feature reduces friction by providing quick access to your most frequently used data items, letting you focus on the transformation logic rather than navigation.

Click through to see how you can access the Recent data menu and what it includes.

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Spark Schema Inference in Production

Published 2026-02-23 by Kevin Feasel

Miles Cole shares some advice:

To show the impact I want to highlight a benchmark that included Fabric Spark on a single 19GB CSV input file (100M Contoso dataset, sales table) for the benchmark. While there were a number of issue with this benchmark that inadvertently make Spark appear to be slow, this is only focused on the impact of inferring schema and practical recommendations.

Read on to see a performance problem that schema inference brings up. I’d also want to mention the risk of data updates blowing up your well-laid plans as a risk. Schema inference is a double-edged sword: it can be convenient and open up new approaches to development, but can just as easily cause unexpected failures.

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