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Category: Microsoft Fabric

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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Loading JSON into a Microsoft Fabric Eventhouse

Published 2025-05-23 by Kevin Feasel

Christopher Schmidt loads some data:

In the era of big data, efficiently parsing and analyzing JSON data is critical for gaining actionable insights. Leveraging Kusto, a powerful query engine developed by Microsoft, enhances the efficiency of handling JSON data, making it simpler and faster to derive meaningful patterns and trends. Perhaps more importantly, Kusto’s ability to easily parse simple or nested JSON makes it easier then ever to extract meaningful insights from this data. The purpose of this blog post is to walk through ways that JSON data can be loaded into Eventhouse in Microsoft Fabric, where you can then leverage Kusto’s powerful capabilities for this. I’ve tried this a few different ways, and the below approach is the fastest, most efficient low-code way to ingest the data into the Eventhouse. As JSON inherently supports different schemas in a single file, the expectation here is that we have a json file with varying schemas within a single file, and we would like to load this into our Eventhouse for efficient parsing with KQL.

Read on for the process.

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Materialized Lake Views in Microsoft Fabric

Published 2025-05-22 by Kevin Feasel

Balaji Sankaran has a new announcement:

We are excited to announce Materialized Lake views (MLV) in Microsoft Fabric. Coming soon in preview, MLV is a new feature that allows you to build declarative data pipelines using SQL, complete with built-in data quality rules and automatic monitoring of data transformations. In essence, an MLV is a persisted, continuously updated view of your data that simplifies how you implement multi-stage Lakehouse processing, commonly referred to as medallion architecture.

Read on to see how it works and some of its capabilities.

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Cosmos DB in Microsoft Fabric

Published 2025-05-21 by Kevin Feasel

Anitha Adusumilli has an announcement:

Building on the momentum from the launch of SQL database in Fabric, we are expanding databases workload in Fabric with this new addition. You can now store semi-structured NoSQL data in Cosmos DB in Fabric, alongside your relational data in SQL databases, enabling a unified data platform for your applications. This further positions Fabric as a complete data platform to handle all your organizational needs, from operational to analytics and BI.

Good news: Microsoft is helping us find the exact limit for the credit cards we’re using to pay for Fabric.

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The Unreliability of Microsoft Fabric

Published 2025-05-20 by Kevin Feasel

Brent Ozar points out some major issues:

The link https://aka.ms/fabricsupport takes you to a localized status page that almost always shows all green checkmarks – even when the service is on fire. During last month’s 12+hour overnight outage, people were screaming on Reddit overnight that things were down, but the status dashboard was showing all green. When Microsoft employees woke up, they asked if people were still having problems – and then eventually got around to updating the status page to reflect the outage when it was clear that things were really borked.

Redditors have resorted to relying on reporting Fabric outages to Statusgator, who then tracks the time gap between a burst of user outage reports, to the time Microsoft actually updates their status page – and it ain’t pretty:

Click through for Brent’s take and an embarrassingly bad post-mortem. Given that Microsoft Fabric is a software-as-a-service product, there’s an inherent level of trust necessary in using it: you’re relying upon the platform team to ensure things are running smoothly and that you get what you’re paying for. Incidents like this erode that trust. Outages themselves are bad but they do happen. The real problem is in not embracing the outage: be clear with customers on current status and cause, and ensure people can easily see the history of events.

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Comparing Data Importation Modes in Fabric Semantic Models

Published 2025-05-15 by Kevin Feasel

Marco Russo has a guide:

When I presented “Choosing Between Import Mode, Direct Lake, and Composite Models” at Fabric Conf 2025 in Las Vegas, the room overflowed, and the session was not recorded. I promised to publish the material once the new Direct Lake + Import composite model became available. This post follows the structure of that (now re‑recorded) session.

I prepared a recap for this blog post, but I suggest you watch the full video!

Check out the video and Marco’s guidance.

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PARSE_SYNTAX_ERROR in Microsoft Fabric Notebooks

Published 2025-05-14 by Kevin Feasel

Olivier Van Steenlandt runs into an error:

As mentioned earlier, I have been playing around with Microsoft Fabric intensively in the past few months. During this period, I ran into a specific issue with one of my notebooks. What happened? Well, I was starting on a new notebook in the evening and life happened… So I stopped playing around to do something else.

A few days later, I wanted to continue my work and remembered that I was required to change something in my data load from a csv file.

Read on for the cause of this error. It’s something that can affect anyone at any time. Even you. Well, probably not you, but the person next to you? Yeah, even that person.

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