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Category: Microsoft Fabric

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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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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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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Automated Table Statistics on Delta Tables in Microsoft Fabric

Published 2025-05-30 by Kevin Feasel

Santhosh Kumar Ravindran makes an announcement:

We’re thrilled to introduce Automated Table Statistics in Microsoft Fabric Data Engineering — a major upgrade that helps you get blazing-fast query performance with zero manual effort.

Whether you’re running complex joins, large aggregations, or heavy filtering workloads, Fabric’s new automated statistics will help Spark make smarter decisions, saving you time, compute, and money.

Click through to see what’s included, as well as the limitations associated with this. You can still create manual statistics if you’d like, so on the whole, I approve.

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Mirroring in Microsoft Fabric

Published 2025-05-28 by Kevin Feasel

Swetha Mannepalli explains how mirroring works in Microsoft Fabric:

Data is complex. It’s often scattered across multiple systems, stored in various formats, locked in silos and changing all the time — making it difficult to harness its full potential. Bringing this data together to power AI and BI workloads typically requires time-consuming ETL processes, custom pipelines, and deep technical expertise. There’s no simple way to get started…until now. 

Click through for more details. And I get the complaint that the term “mirroring” has a different meaning in SQL Server, and that Fabric mirroring from a SQL Server instance doesn’t actually use the mirroring technology that has been deprecated since 2012 but still remains in the product because reasons. But in fairness, there are only so many synonyms people can use. Which means, three years from now, marketing will rename the feature to “replication.”

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Common Data Transformations in Microsoft Fabric

Published 2025-05-28 by Kevin Feasel

Nikola Ilic takes us through several data transformations:

In the lakehouse, for example, you can transform the data by using PySpark, but also Spark SQL, which is VERY similar to Microsoft’s dialect of SQL, called Transact-SQL (or T-SQL, abbreviated). In the warehouse, you can apply transformations using T-SQL, but Python is also an option by leveraging a special pyodbc library. Finally, in the KQL database, you can run both KQL and T-SQL statements. As you may rightly assume, the lines are blurred, and sometimes the path is not 100% clear.

Therefore, in this article, I’ll explore five common data transformations and how to perform each one using three Fabric languages: PySpark, T-SQL, and KQL.

Click through for those transformations, such as extracting date parts, fixing casing, and pivoting data.

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Trying out Microsoft Fabric Data Agents

Published 2025-05-27 by Kevin Feasel

Wolfgang Strasser gives a generative AI solution built into Microsoft Fabric a try:

Today, I wanted to give the new Fabric Data Agents a try. According to the documentation, a Fabric Data Agent is defined as follows:

Data agent in Microsoft Fabric is a new Microsoft Fabric feature that allows you to build your own conversational Q&A systems using generative AI. A Fabric data agent makes data insights more accessible and actionable for everyone in your organization. With a Fabric data agent, your team can have conversations, with plain English-language questions, about the data that your organization stored in Fabric OneLake and then receive relevant answers. This way, even people without technical expertise in AI or a deep understanding of the data structure can receive precise and context-rich answers.

Let’s give it a try and build our first Data Agent.

Click through for the pre-requisites, the setup process, and how everything looked for Wolfgang.

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