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Day: March 17, 2026

Managing Eventhouses and Environments with MicrosoftFabricMgmt

Published 2026-03-17 by Kevin Feasel

Rob Sewell continues a series on the MicrosoftFabricMgmt module. First up is a dive into the Kusto world:

Real-Time Intelligence (RTI) is Microsoft Fabric’s answer to streaming data workloads. If you are ingesting telemetry, IoT data, clickstreams, or any high-velocity data that needs querying with low latency, this is the part of Fabric you want. MicrosoftFabricMgmt supports the full set of RTI resources: Eventhouses, KQL Databases, KQL Dashboards, KQL Querysets, and Eventstreams.

Rob then pivots to creating an environment from scratch:

Over the past few posts we have worked through a number of the item choices that you can use in the MicrosoftFabricMgmt module. Today I want to bring it all together into a single, practical script that provisions a complete Fabric environment from scratch.

This is the kind of script I could use when setting up a new project. It is repeatable, idempotent (safe to run multiple times), fully logged, and handles errors gracefully.

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Rolling Average Calculation via DATE_BUCKET()

Published 2026-03-17 by Kevin Feasel

Koen Verbeeck writes some code for SQL Server 2022 or later:

In the Microsoft Fabric Warehouse, a new T-SQL function was recently added: the DATE_BUCKET function. With this function, you can group dates into pre-defined buckets. This allows you to easily calculate aggregates that use the GROUP BY clause over these buckets, greatly simplifying the T-SQL statements for analytical use cases.

Click through for a demo. Koen mentions that this is also now available in the Microsoft Fabric Warehouse. Once you know how DATE_BUCKET() works, it’s pretty powerful. But I also think that the function is a bit confusing to use.

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Finding Power BI Measures without Column Relationships

Published 2026-03-17 by Kevin Feasel

Zoe Douglas gives a visual cue that not all is well:

Have you ever put a measure on a visual with a column from a table and found it repeated the same value for every row and the total? This indicates there is no relationship for that measure and the column. And that simply may be the case, as in, there is no relationship to create. Let’s look at how we can account for that in a different way, by showing a placeholder value such as ###.

Read on to see how.

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Migrating SQL Server Service Accounts to gMSA

Published 2026-03-17 by Kevin Feasel

Deepthi Goguri takes advantage of Group Managed Service Accounts in Windows:

A Service Account is something that applications like the SQL Server, IIS, or scheduled tasks need to run under using Microsoft Active Directory. These are the regular domain user accounts, where the passwords needs to be manually managed and rotated. As these needs to be manually updated, downtime to the services are required if the password needs to be changed. Not only that but syncing these passwords across multiple servers can be an issue. This problem is resolved by using the Standalone Managed Service Account as Windows can manage the password automatically.

Read on to learn more about single MSAs and group MSAs, and some tips and limitations.

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Dealing with Multiple Fabric Capacities

Published 2026-03-17 by Kevin Feasel

Jon Lunn provides some guidance:

You know you can have more that one capacity? Most of the clients I’ve interacted with, even since the Power BI capacity days, they have just purchased one big old capacity, and assigned it to every workspace they needed. There have been a few clients that have had multi-region capacities, spun up across the globe for thing likes, billing to specific cost centres and regions and data ownership and sovereignty issues, but for those that don’t have those issue, they just get a big capacity.

Jon provides some guidance on environment-based capacity planning. Even within an environment, there may be cases for carving out explicit capacity, such as data science activities that are occasional but potentially disruptive.

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Partitioned Compute and Fabric Dataflow Performance

Published 2026-03-17 by Kevin Feasel

Chris Webb performs a test:

Partitioned Compute is a new feature in Fabric Dataflows that allows you to run certain operations inside a Dataflow query in parallel and therefore improve performance. While UI support is limited at the moment it can be used in any Dataflow by adding a single line of fairly simple M code and checking a box in the Options dialog. But as with a lot of performance optimisation features (and this is particularly true of Dataflows) it can sometimes result in worse performance rather than better performance – you need to know how and when to use it. And so, in order to understand when this feature should and shouldn’t be used, I decided to do some tests and share the results here.

Click through for the test, the result, and an open door for subsequent analysis.

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