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Day: August 24, 2026

What to DO When Out of Capacity in Microsoft Fabric

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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Working in Batches in SQL Server

John Deardurff has some advice:

I’ve been reviewing Azure SQL Database performance guidance recently and came across Microsoft’s documentation on How to Use Batching to Improve Application Performance. While the article focuses on Azure SQL Database, the same principles of batching transactions for better performance apply equally well to SQL Server and Azure SQL Managed Instance.

The reason this topic caught my attention is that batching doesn’t just improve performance. It can reduce blocking, minimize rollback pain, improve transaction log efficiency, and potentially lower costs in cloud environments. That’s a pretty good return on investment for a relatively simple coding change. Here is the SQL Script that I use for this demonstration. Feel free to test for yourself. (It is a text file, so you will have to save it as a .sql file.)

Batching is especially important on delete operations against larger tables, where you don’t remove enough data to make TRUNCATE TABLE a viable alternative (or where you don’t have permissions to truncate). But one thing to keep in mind is that index design matters for batch operations. If you don’t have a good index, your first batches will be fast but they will gradually slow down as SQL Server needs to scan an increasingly large range to find the next set of rows to update. I wrote about this quite a while ago when putting together a talk on near-zero downtime T-SQL operations.

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The Pain of Microsoft Fabric Deployment Pipelines

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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Validating Selective Deployments in Microsoft Fabric

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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Semantic Link Labs UI Updates

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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