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Category: Microsoft Fabric

An Overview of the Fabric Unified Admin Monitoring Tool

Published 2026-02-23 by Kevin Feasel

Rob Sewell lays out some information:

When you are responsible for a Microsoft Fabric tenant, it will not be very long before you are facing many questions.

Questions like:

  • How is my capacity being used?
  • Which workspaces are consuming the most resources?
  • What are my users actually doing?
  • When are my peak usage times?

You can scabble around in the Admin portal and try to piece together the answers, but it is a bit like trying to navigate a city with a paper map — you can get there eventually, but it is slow and painful, and you will probably miss some things along the way.

Read on to see how FUAM can help answer these sorts of questions.

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Official Support for fabric-cicd Tool

Published 2026-02-20 by Kevin Feasel

Yaron Pri Gal announces support for a library:

Today, we’re announcing that fabric‑cicd—the open‑source Python deployment library for Microsoft Fabric—is now an officially supported, Microsoft‑backed tool for CI/CD automation across Fabric workspaces.

Over the past year, fabric‑cicd has rapidly evolved through collaboration with engineering, CAT, MVPs, enterprise customers, and the community. Growing usage, strong sentiment across internal and external channels, and adoption by organizations building enterprise‑grade deployment pipelines helped solidify its value within the Fabric ecosystem.

Read on to learn what this means.

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Load Testing Microsoft Fabric Redux

Published 2026-02-18 by Kevin Feasel

Reitse Eskens goes back to the well:

For those of you who have been following this blog for a long time, you may have read the posts on Fabric where I’m comparing the F64 Trial with the F2, and other shenanigans. Because Fabric keeps evolving, and new releases keep coming that improve or change the behaviour, I felt it was only fair to give Fabric a new run for its capacities.

The idea is not to create a solution that works as quickly as possible. It’s not the goal to tune Fabric, nor to get the most excitement for your Euro. The main goal of this blog (and the session that I’m presenting on this topic), is to show you the differences, the error messages and where to look when you get lost. Because, for all its intents and purposes, error handling is still tricky, and it seems to be very hard to get rid of “Something went wrong” messages.

I appreciate Reitse’s localization of the well-known phrase “most win for your Yen.”

Click through for plenty of graphs and lots of testing.

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When Warehouse Beats Lakehouse

Published 2026-02-18 by Kevin Feasel

Gilbert Quevauvilliers runs a test:

After my previous blog post on the different semantic model options and at the same time working with a Fabric customer, it got me thinking which is faster and which consumes less capacity when ingesting data into Power BI either via the SQL Endpoint to a Lakehouse or a query from the Warehouse.

Below you will find the information which I found very interesting indeed.

For both the Lakehouse and Warehouse source CSV’s there was a total of 237,245,585 rows.

Click through for the numbers, and a scenario in which the warehouse loads data faster than a lakehouse.

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License Types now Workspace Types in Microsoft Fabric

Published 2026-02-17 by Kevin Feasel

Nicky van Vreonhoven notices a change in language:

Just a quick post because I noticed a change in the Fabric UI, specifically in the Workspace settings.

I am working on a demo for my Power BI Gebruikersdagen session, and wanted to switch a workspace to Fabric capacity. I noticed that the setting License type has changed, and is now called Workspace type.

Read on to see where this has changed and a few more notes from Nicky.

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Snapshot Reporting in Microsoft Fabric via Fabric Pipelines

Published 2026-02-17 by Kevin Feasel

Kenneth Omorodion builds a Dataflows Gen2 pipeline:

In a previous tip, I described how we can implement snapshot reporting using Microsoft Fabric Dataflow Gen2. In this article, I will describe how to achieve the same using Microsoft Fabric Pipelines. I previously described how important snapshot reporting can be in Business Intelligence reporting. Some reasons why developers/engineers might prefer to leverage a Fabric pipeline instead of a Dataflow Gen 2 include considerations around cost efficiency and data volumes.

My strong preference is still to do this in code (notebooks, Spark jobs), but at least Dataflows Gen2 aren’t literally 100x slower than the alternatives anymore.

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Predictive Analytics with Power BI and Microsoft Fabric

Published 2026-02-16 by Kevin Feasel

Ruixin Xu puts together a how-to guide:

Across industries, teams use Power BI to understand what has already happened. Dashboards show trends, highlight performance, and keep organizations aligned around a shared view of the business.

But leaders are asking new questions—not just what happened, but what is likely next and how outcomes might change if they act. They want insights that help teams prioritize, intervene earlier, and focus effort where it matters. This is why many organizations look to enrich Power BI reports with machine learning.

This challenge is especially common in financial services.

Consider a bank that uses Power BI to track customer activity, balances, and service usage. Historical analysis shows that around 20% of customers churn, with churn tied to factors such as customer tenure, product usage, service interactions, and balance changes.

Click through for the architecture example and process. The actual model is a LightGBM model, which is generally fine for two-class classification.

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Adaptive Time Series Visualization in Microsoft Fabric

Published 2026-02-13 by Kevin Feasel

Devang Shah and Slava Trofimov show off a design pattern:

This design pattern provides intuitive, interactive Fabric-native experiences for any user:

  • Intelligent time binning: Handle billions of data points by automatically grouping them into optimal intervals.
  • Time brushing: Zoom in any period with drag-and-select interactions.
  • Multi-metric comparison: View multiple time series side by side across different assets.
  • Flexible aggregation: Switch between average, min, max, and sum with a single selection.
  • Anomaly detection: KQL queries detect unusual patterns in your time series with no ML expertise required.
  • Statistical insights: View descriptive statistics and correlations.
  • Contextualization: Bring asset hierarchies, tag metadata, and definitions directly into the report for richer interpretation.

Read on to learn more about the pattern and how it works. There are a lot of moving parts to get right, but the end result looks impressive.

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