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Author: Kevin Feasel

What’s Forthcoming for DBAs in SQL Server 2025

Published 2025-06-05 by Kevin Feasel

Stephen Planck has a list:

There is a lot to be excited about in SQL Server 2025! When thinking about features that may not get as much attention as others, yet will make a real difference in the lives of DBAs, I have selected my top 5 enhancements for SQL Server 2025. These improvements may not make the headlines, but they address pain points we’ve all experienced as DBAs. Please let me know if I’ve left any of your favorites off the list.

Read on for the list. It’s not a set of fancy new features, though there are some quality of life improvements here.

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Creating Fabric Databases via Powershell

Published 2025-06-05 by Kevin Feasel

Rob Sewell has a trio of posts. First up, Rob creates a warehouse:

We are going to use [FabricTools, a PowerShell module that simplifies the management of Microsoft Fabric resources. You can find it on GitHub at[FabricTools provides functions to create, update, and delete Fabric warehouses and other resources. It has been developed and is maintained by members of the community.

Next up is a lakehouse:

Having created a Microsoft Fabric workspace using PowerShell, assigned users to it using PowerShell, and created a Microsoft Fabric Warehouse using PowerShell, we now turn our attention to creating a Microsoft Fabric Lakehouse using PowerShell. These posts all look remarkably similar 🙂 and thats because the functionality they are showing is exactly same..

Just like dbatools and dbachecks creating functions that follow the same pattern makes it easier to learn. This is why FabricTools has chosen to use the same pattern for creating Fabric resources using PowerShell.

And from there is a Fabric SQL Database:

A Fabric SQL Database is a SQL Database that is hosted in Microsoft Fabric. It provides a fully managed SQL database service that allows you to store and query your data using SQL using the same SLQ Engine as Azure SQL Database.

Click through for examples of all three.

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Consumer Group Rebalancing in Kafka and KIP-848

Published 2025-06-04 by Kevin Feasel

Jonathan Lacefield gives us a heads-up:

Historically, Kafka has relied on what we now call the “classic” rebalance protocol. This protocol evolved, as it was initially dominated by the “eager” assignment strategy. Eager rebalancing worked on a stop-the-world principle: Any change in group membership (consumer joining/leaving) or topic metadata triggered a complete halt. All consumers revoked their partitions, a leader computed a new assignment, and partitions were redistributed before processing could resume. This caused significant downtime, especially in dynamic environments.

To mitigate this, the cooperative assignment strategy was introduced within the classic protocol. Cooperative rebalancing reduced downtime by allowing consumers to keep partitions unaffected by the rebalance, revoking only those needing reassignment.

Read on to learn about some of the challenges that exist with rebalancing, and what KIP-848 promises to do.

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Simplifying Calculations with the APPLY Operator

Published 2025-06-04 by Kevin Feasel

I have a new video:

In this video, I show how we can use the APPLY operator to remove redundancy in the SELECT clause and simplify complex calculations, all with zero performance impact.

This is, as I’ve said in the past, my favorite use case for the APPLY operator. As I’ve become older and (even) more crochety, I’ve sided more and more with “easy to read” versus “runs faster” for code. And when you get “easy to read” with no impact on “runs faster,” I’m all in.

The accounting scenario I show may be a fairly extreme case, but I’d bet that queries similar to this abound in almost any company. A big part of why complex reporting queries are so complex comes from repetition of calculations.

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Populating Microsoft Fabric Data Agents with Semantic Model Synonyms

Published 2025-06-04 by Kevin Feasel

Marc Lelijveld explains some terms:

It was only yesterday, that I wrote a blog post on Semantic Models as a source for Fabric Data Agents. Not much time has passed, since I learned that Fabric Data Agents does not (always) respect the Synonyms that have been added to a Semantic Model. As a result, the Data Agent may start creating implicit measures, not respecting the definitions and logic in the explicit measures that are part of the Semantic Model.

Long story short, I think we should be able to do better! Therefore, I created a Notebook that helps you to setup Data Agents, collect additional information from your Semantic Model and populate that information automatically as AI notes to the Data Agent.

Read on for the notebook and some additional explanation.

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Invoking Child Pipelines in Microsoft Fabric

Published 2025-06-04 by Kevin Feasel

Meagan Longoria spots the fork in the road:

At the moment there are two activities in Fabric pipelines that allow you to execute a “child” pipeline. They are both named “Invoke Pipeline” but are differentiated by the labels “Legacy” and “Preview” in parentheses.

Read on to learn more about these two and why choosing the new one may not always be the best option for you, at least not yet.

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Native Power BI Write-Back in Microsoft Fabric

Published 2025-06-04 by Kevin Feasel

Jon Vöge comes full-circle:

Three years ago, write-back to Power BI was my gateway into the Power BI community.

Power Apps embedded into Power BI, enabling write-back to Sharepoint, Azure SQL and Fabric, and sharing those solutions with the community, have always been some of the most fun I’ve had with “work”.

However.

While Power Apps are relatively easy to build, the solution architecture quickly becomes complex. Especially when you consider governance, CI/CD and licensing, all of which balloons in size when you are forced to integrate with a new platform (Dataverse/Power Platform) to solve a seemingly small issue in a Power BI report.

Click through to see the new way to do this. It’s been a point of frustration for me that, for so long, it has been such a challenge to allow a user to annotate or augment data in Power BI.

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Writing a Python Data Frame to a Lakehouse Table

Published 2025-06-04 by Kevin Feasel

Gilbert Quevauvilliers continues a series on Python notebooks and DuckDB:

In this blog post I am going to explain how to loop through a data frame to query data and write once to a Lakehouse table.

The example I will use is to loop through a list of dates which I get from my date table, then query an API, append to an existing data frame and finally write once to a Lakehouse table.

Click through for the code, as well as a sample notebook you can use.

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What’s New in Apache Spark 4.0

Published 2025-06-03 by Kevin Feasel

Ram Ghadiyaram looks at recent updates to Apache Spark:

Hurray! Apache Spark 4.0, released in 2025, redefines big data processing with innovations that enhance performance, accessibility, and developer productivity. With contributions from over 400 developers across organizations like Databricks, Apple, and NVIDIA, Spark 4.0 resolves thousands of JIRA issues, introducing transformative features: native plotting in PySpark, Python Data Source API, polymorphic User-Defined Table Functions (UDTFs), state store enhancements, SQL scripting, and Spark Connect improvements. This report provides an in-depth exploration of these features, their technical underpinnings, and practical applications through original examples and diagrams.

Click through to see what’s on the list of major features.

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Apache Spark 3.5 Support in Azure Synapse Analytics

Published 2025-06-03 by Kevin Feasel

Arshad Ali has an announcement:

You can now create Azure Synapse Runtime for Apache Spark 3.5. The essential changes include features which come from upgrading Apache Spark to version 3.5 and Delta Lake 3.2. Please review the official release notes for Apache Spark 3.5 to check the complete list of fixes and features. In addition, review the migration guidelines between Spark 3.4 and 3.5 to assess potential changes to your applications, jobs and notebooks. 

Credit where credit is due: I’ve made light of the utter lack of work on Azure Synapse Analytics since Microsoft Fabric’s release. But hey, they did a thing. Granted, the impetus behind this was to “prepare for migrating to Microsoft Fabric Spark.”

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