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Category: Microsoft Fabric

Dynamically Changing Fabric Data Warehouse SQL Pools

Published 2026-08-27 by Kevin Feasel

Gilbert Quevauvilliers saves some money:

After reading about the new SQL Pools feature for Warehouses in Fabric, I had an idea, if I could change the SQL Pool configuration based on the expected query load, I could then consume less capacity and have better performance.

https://learn.microsoft.com/en-us/fabric/data-warehouse/custom-sql-pools

Here is an Example I thought of below.

  • When the ETL load is running optimize the SQL pool for writing as typically data is being inserted.
  • After the ETL load and for the rest of the day, almost all queries are read by the Warehouse, so change the SQL pool to be read optimized.

Click through for a Python notebook that does this.

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INFORMATION_SCHEMA and the Fabric Warehouse

Published 2026-08-27 by Kevin Feasel

Louis Davidson bangs his head against a wall:

When we decided to use T-SQL and a Fabric Data Warehouse for our ETL, I started thinking about generating the code with the metadata in the system catalog views or the INFORMATION_SCHEMA. Having done this sort of thing before in SQL Server over the years, it seemed really straightforward. And it kind of is, until it isn’t.

In this blog I want to show you a few ways you need to understand how working with metadata and temp tables varies (sometimes wildly) from the comfortable SQL Server environment and language you know very well, and give tips on how to get around these differences.

Click through for some of the fun you can have with a distributed SQL Server-like product.

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What to DO When Out of Capacity in Microsoft Fabric

Published 2026-08-24 by Kevin Feasel

James Serra hits the ceiling:

Microsoft Fabric makes it wonderfully easy to put many analytics workloads on one platform. Power BI, data engineering, warehousing, data science, real-time analytics, Copilot, and other experiences can all share the same Fabric capacity. That is a big advantage, but it also creates an architectural question that does not get much attention until something goes wrong: what should you actually do when a capacity starts running out of room? The answer is not always “buy a bigger capacity.” Sometimes you should optimize, sometimes scale up, sometimes scale out, and sometimes isolate the workload causing the problem.

Read on for an explanation of what it means to “run out of capacity” and a quick overview of four options available to you.

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The Pain of Microsoft Fabric Deployment Pipelines

Published 2026-08-24 by Kevin Feasel

Meagan Longoria has a list:

Fabric deployment pipelines look like they solve CI/CD for Fabric content, especially for people who prefer a GUI over writing code to handle deployments, but the implementation has enough structural gaps that they fall apart for several real deployment workflows. Even when they do work, the UI isn’t always intuitive.

Every piece of active software carries a backlog of feature requests and known limitations, and deployment pipelines get new capabilities on a regular basis. Everything below reflects how deployment pipelines behave as of August 2026. Some of it may have changed by the time you’re reading this, so check Microsoft’s docs for the current state before you plan around any of these.

Click through for the list, as well as a few alternatives that come with their own trade-offs.

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Semantic Link Labs UI Updates

Published 2026-08-24 by Kevin Feasel

Chris Webb takes a look:

There’s so much going on in the Fabric community that it can be hard to keep up with it all. Semantic Link Labs is a great example: in the six months or so since I last had a proper look at it my colleague Michael Kovalsky has done a whole load of cool things and it wasn’t until I had a chat with him recently that I realised how much had changed. Most importantly, for someone old-fashioned like me who still likes tools with a UI, a lot of new functionality has been added which has a UI and is usable with minimal coding.

Click through to see what’s available.

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Validating Selective Deployments in Microsoft Fabric

Published 2026-08-24 by Kevin Feasel

Matt Collins shares some advice:

Selective deployments with the fabric-cicd python library are highly useful for shipping just the items that actually changed in your CI/CD process. Unfortunately, by default, it also tells you that a deployment succeeded when nothing was deployed at all.

This blog showcases an example where we selectively deployed Fabric items to upper environments, not realising that the dev ops pipeline reported as “successful” but did not contain our intended changes. We will then dig into some quality checks that highlighted a bigger error in the way Microsoft Fabric handles item naming in a repository.

You’ll learn how to help safeguard from human error in selective Fabric deployments, as well as create useful build validations in Azure DevOps that help to keep your repository resource names clean and fit-for-purpose. All this is achieved through two simple CI/CD pipelines.

Read on to learn more.

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Surrogate Keys in Fabric Data Warehouse

Published 2026-08-20 by Kevin Feasel

Louis Davidson does some more digging:

Creating the simplest of tables, the first thing I start thinking about is making sure that the data is going to be protected from the user. Users (including myself on my own projects when I have my user hat on) don’t notice when they start inserting poor quality data sometimes. Oops, I hit F5 twice, I wonder if that will affect my data? Without proper constraints, it probably will.

In this second entry, I want to cover a few things about handling surrogate values that will help you avoid some of the (in retrospect) kind of dumb expectations that I had. Fabric Data Warehouse T-SQL feels so much like SQL Server Relational T-SQL that some stuff like choosing a surrogate key makes me think I am missing something.

One of the things Louis mentions is how the values get inserted and how it looks like they’re in different ranges. This makes sense, as each distributed node likely has its own range of identity values, similar to the way merge replication would work with identity keys to prevent overlap.

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Configuring OneLake Security for a Microsoft Fabric Lakehouse

Published 2026-08-14 by Kevin Feasel

Gilbert Quevauvilliers locks things down:

In this blog post, I will show you how to configure OneLake security for a Microsoft Fabric Lakehouse. We will remove the default broad-access role, grant the required Lakehouse access, configure object-, row-, and column-level security, and test the results. The goal is to create a secure-by-default setup in which users can access only the data they need.

Click through for the guide.

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Accessing Fabric Semantic Model Data in Excel

Published 2026-08-12 by Kevin Feasel

Teo Lachev comes full-circle:

Ask a business user where they want to work with the data, and Excel would probably top the list. Regardless of how hard we try to lure users away from it, Excel remains the endpoint of self-service BI for many organizations. I have clients who have built custom apps or purchased Excel add-ins simply to get data into Excel—or manipulate it once it gets there.

So rather than fighting Excel, let’s look at the options Microsoft provides for bringing governed Power BI data into it. In particular, I’m interested in getting data from Fabric semantic models into Excel.

Click through for five options, including their pros and cons.

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