Category: Microsoft Fabric

Recovering Deleted Items in Microsoft Fabric

Published 2026-06-01 by Kevin Feasel

Let’s be honest: how many times have you accidentally deleted something? Either on your laptop, in a database or in a SaaS product.
It happens. We’re all humans (unless you allow agents to do all your work for you), and mistakes happen.

Until recently, when you deleted an item in Fabric, it was gone. Poof. Done. No grace period.

And that was a bit scary, to be honest, but now we have a new option to help us recover from oopsies!

The answer to Reitse’s question is “far too often for me to want to admit out loud.”

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Partitioned Compute with Fabric Dataflows Gen2

Published 2026-06-01 by Kevin Feasel

Chris Webb continues to test out Fabric Dataflow Gen2 performance:

In the first part of this series I showed how the Concurrency setting in a Fabric Dataflows Gen2 can affect refresh performance when there are multiple queries inside the dataflow. In this post I will show how, with Partitioned Compute, this setting can also affect the performance of a single query within a dataflow.

To test this I created a dataflow with one query, a modified version of the query that I used in this post from earlier this year which returns a table with ten rows and calls a function with a built-in delay of 60 seconds on each row.

This is a preview feature but Chris shows a simple but effective test to demonstrate how this capability works.

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Creating a Private Link in Microsoft Fabric

Published 2026-05-28 by Kevin Feasel

Gilbert Quevauvilliers locks down a Fabric environment:

In this blog post, I am going to walk through how I created a Private Link in Microsoft Fabric so that access routes over Microsoft’s private backbone network instead of the public internet.

NOTE: It is a bit of a longer blog post as there are quite a few things that need to be configured.

NOTE II: In the next blog post I will show you how to connect from a Public Workspace to a Private Link Workspace using a Managed Private Endpoint

I wanted to document the end-to-end process because there are a few moving parts across Microsoft Fabric and the Azure Portal. The key items I needed to configure were the Fabric tenant setting, the Fabric private link service, the virtual network, a test virtual machine, the private endpoint, and finally DNS testing from inside the virtual network.

There’s a lot more here than ticking a couple of checkboxes or selecting a few radio button options.

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Concurrent Evaluation with Microsoft Fabric Dataflows Gen2

Published 2026-05-26 by Kevin Feasel

Chris Webb runs multiple jobs at once:

Did you know that if your Fabric Dataflows Gen2 contains several queries then you can control how many of them are evaluated in parallel when your dataflow refreshes? In this series I’ll look at how how you can do this and how it may result in better performance – at least in some cases.

Let’s start with the basics. I created a Dataflow Gen2 with ten queries which each returned a table of one row and one column after one minute. I used the #table function to generate the table without connecting to a data source, code from this post to add the delay and the trick in this post to make sure the delay was only applied when the dataflow refreshed. The output of each query was loaded to a Fabric Warehouse.

Click through for a demonstration.

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Explaining the Fabric Ontology

Published 2026-05-21 by Kevin Feasel

James Serra takes us through a big word:

For years, most data conversations have started with tables. We ask where the data lives, what columns are available, how the joins work, and whether the data is in a warehouse, lakehouse, semantic model, or some other system. That makes sense, because tables are how most of us have worked with data for decades. But tables are not how the business thinks.

A business thinks in terms of customers, products, orders, shipments, assets, flights, runways, employees, policies, and actions. The problem is not usually a lack of data. The problem is a lack of shared meaning. Organizations often have the same business concept represented multiple ways across teams and systems, creating what I would call semantic drift. Sales may define a customer one way. Finance may define it another way. Operations may have yet another version in a different system with different keys, names, and assumptions. That is exactly where Fabric Ontology becomes important. It is designed to close the gap between physical data structures and business meaning.

Microsoft is a bit late to the ontology game and their current concept of an ontology shows. I can understand where they’re going but they still have a ways to go.

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CLUSTER BY in Microsoft Fabric Data Warehouse

Published 2026-05-21 by Kevin Feasel

Nikola Ilic shows off a relatively new feature:

The first thing every Fabric architect reaches for in this situation is the usual playlist: check the query plan, look at the joins, validate the statistics, maybe scale up the capacity. All worth doing, but none of those things addressed what was actually happening: the warehouse was scanning the entire table for every filtered query, because there was no way to tell it which Parquet files actually contained the rows we cared about.

However, Microsoft shipped data clustering in preview at the end of November 2025, and the entire conversation changed.

In this article, I want to walk you through what data clustering is, how it works under the hood, and most importantly, I’ll show you a real demo on a 100-million-row clickstream table that you can run in your own warehouse. No abstractions, no marketing numbers, but actual T-SQL you can paste.

Some of the notes Nikola mentions remind me of some of the rules around making columnstore indexes work and for much of the same reason. But as Nikola’s demo shows, this is definitely a “You must be this tall to ride the ride” feature, and unless you’re talking about quite large fact tables with (at a minimum) billions of rows of data, the benefit mostly comes from reducing CUs rather than wall clock time improvements.

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Exceeding the Capacity Limit for Power BI Dataset Refreshes

Published 2026-05-18 by Kevin Feasel

Chris Webb explains an error:

If you have a lot of Power BI semantic models that are scheduled to refresh at the same time in the Service then you may find that some of them fail with the following error:

You’ve exceeded the capacity limit for dataset refreshes. Try again when fewer datasets are being processed.

[Note: “dataset” is the old name for a Power BI semantic model. Someone should update the error message.]

Read on to see what can cause this error and what you can do about it.

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Metadata-Driven Frameworks for Change Detection in Microsoft Fabric

Published 2026-05-14 by Kevin Feasel

Kevin Chant builds a table:

I had various options for this months contribution due to my experience with various change detection solutions. Including Azure Synapse Link for SQL Server 2022. Which I covered in previous posts. Including one that covered some excessive file tests for Azure Synapse Link for SQL Server 2022.

In the end I decided to cover developing metadata-driven frameworks for Microsoft Fabric. Due to the fact that it is such a hot topic for multiple reasons. One of which is the growing availability of open-source, metadata-driven frameworks for Microsoft Fabric.

Read on for three such frameworks and some advice on how to use them.

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Using Fabric Data Wrangler for Testing

Published 2026-05-13 by Kevin Feasel

Kristina Mishra checks out some data:

Data Wrangler has been available for awhile now, but I’ll be honest, it’s not something we’ve been actively using. We’ve been heads down on time-sensitive projects for over a year and needless to say, our cup runneth over. Recently we’ve had a bit of respite and I decided to see how we could use Data Wrangler within the context of our current Microsoft Fabric data warehouse (i.e. medallion layer lakehouses).

Data Wrangler has a lot of cool features that will give you code snippets for what you want to do, but I wanted to use it a different way. I wanted to have an easy way to do a quick check for dimension tables. I also wanted an easy-peasy way for others, some of whom are not developers, to be able to do quick sanity check of the data.

Click through to see how it works.

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Microsoft Fabric Eventhouse Caching and Retention

Published 2026-05-11 by Kevin Feasel

Nikola Ilic notes the ephemeral nature of life:

You spin up your first Eventhouse, ingest some IoT data, fire up a KQL query, and it runs fast. When I say fast, I mean embarrassingly fast. A few weeks later, you query data from a couple of months ago, and… it’s still fast, but maybe a tiny bit slower. A year later, the same query starts to feel sluggish. Two years later, you can’t find some of the data at all.

Welcome to the world of tiered storage in Real-Time Intelligence!

And when Nikola mentions how fast data in hot storage is, that’s no exaggeration. It is, to my knowledge, the fastest way of retrieving data in Microsoft Fabric.

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