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Category: Microsoft Fabric

Exploring the Fabric Ontology

Published 2026-03-25 by Kevin Feasel

Jens Vestergaard takes a peek at Ontologies in Microsoft Fabric:

I have been spending a little time with the Microsoft Fabric data agent documentation lately, and one pattern keeps showing up, and it is not just in the official guidance but in community posts from people who have actually tried to deploy these things: the demo runs beautifully. The AI answers questions in plain English, leadership gets excited, the pilot gets approved. Then it hits production. Real users send real questions. The answers start drifting. Numbers that should match do not. The same question returns different results on different days. Trust evaporates faster than it was built.

And almost every time, the root cause is the same thing: the semantic foundation was not solid enough before anyone pointed an agent at it.

That is exactly the problem the Fabric Ontology is designed to address. It is the piece I think most teams will underestimate right up until the moment they need it.

Click through for an explanation. As I continue learning more about the concept of ontologies (not just in Fabric but in general), I’m slowly coming around to the idea. Though it still reminds me a lot of object-oriented programming with a no-code interface.

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dbt and Microsoft Fabric

Published 2026-03-25 by Kevin Feasel

Pradeep Srikakolapu and Abhishek Narain dig into dbt:

Modern analytics teams are adopting open, SQL-first data transformation, robust CI/CD and governance, and seamless integration across lakehouse and warehouse platforms. dbt is now the standard for analytics engineering, while Microsoft Fabric unifies data engineering, science, warehousing, and BI in OneLake.

By investing in dbt + Microsoft Fabric integration, Microsoft empowers customers with a unified, enterprise-grade analytics platform that supports native dbt workflows—future-proofing analytics engineering on Fabric.

I’ll be interested to see if this retains corporate investment longer than some of their open-source collaborations. That’s been a consistent issue over the years: announce some neat integrations with a popular technology, release a couple of versions, and then quietly deprecate it a year or two later. This sounds like it’s less likely to end up in that boat, simply based on how the Fabric team is collaborating compared to, say, the various Spark on .NET efforts over the years.

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An Overview of Major FabCon Announcements

Published 2026-03-24 by Kevin Feasel

Nicky van Vroenhoven lays out some of the most important changes:

I am sure you have seen, there has been a lot of Fabric and Power BI news lately. Not surprisingly, Fabric Conference was also last week!

I won’t list all the updates here, you can read Arun’s blog, or either of the Fabric or Power BI monthly feature summary blogs to go through the whole list:

Click through for a dozen or so major changes that Nicky highlights.

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What’s New with the Fabric SSIS Preview

Published 2026-03-24 by Kevin Feasel

Andy Leonard frames the discussion:

The conversation around SSIS is heating up again.

Some see the signals and conclude SSIS is on the way out. Others point to the strength of the ecosystem and say it is far from done. Both perspectives miss something important.

The introduction of Fabric SSIS public preview does not settle the debate. It reframes it.

I see this as another way of saying, “We know you’re still using SSIS packages but we really don’t want to invest in that any longer, so how about you move it into Fabric until you do finally rewrite things as Fabric Data Pipelines?”

That said, Andy lays out where he sees the current landscape and how there are common issues across Microsoft’s ETL/ELT products, mostly in how people use them.

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Query Folding and Staging in Fabric Dataflows Gen2

Published 2026-03-24 by Kevin Feasel

Chris Webb goes digging:

A few years ago I wrote this post on the subject of staging in Fabric Dataflows Gen2. In it I explained what staging is, how you can enable it for a query inside a Dataflow, and discussed the pros and cons of using it. However one thing I never got round to doing until this week is looking at how you can tell if query folding is happening on staged data inside a Dataflow – which turns out to be harder to do than you might think.

Read on to learn more, and also check out the comment describing an alternative approach to part of Chris’s solution.

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Third-Party Support for OneLake Security

Published 2026-03-23 by Kevin Feasel

Aaron Merrill shares some guidance:

As outlined in our technical whitepaper, ‘The future of data security is interoperability‘, permissions that move with data is the future of data security. As modern data lakes are built on open-source technology like Delta and Iceberg, customers expect to use the analytics engines and services that best fit their needs—without copying data or redefining security. This creates a clear requirement: security must be defined once and enforced consistently everywhere data is consumed.

OneLake security now provides API support for third-party enforcement through an authorized engine model. This release extends the same principles used across Microsoft Fabric to external engines and services. OneLake security is now closer to its vision of defined once, enforced everywhere, even beyond first-party workloads.

Click through for more information.

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Maps in Microsoft Fabric now GA

Published 2026-03-23 by Kevin Feasel

Johannes Kebeck makes an announcement:

When we envisioned Maps in Microsoft Fabric, our goal was to empower any data citizen to analyze data in time and space without any specialized knowledge. Introduced in preview at FabCon Europe 2025, it has since been used by customers across industries creating and sharing map-centric applications. Additional features were added at Ignite 2025, and this week at FabCon Atlanta, Maps in Microsoft Fabric is generally available – along with new capabilities that expand how geospatial data can be modeled, visualized, and operationalized at any scale.

Read on to see what’s new in maps.

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What’s New in SQL Database for Fabric

Published 2026-03-20 by Kevin Feasel

Idris Motiwala makes some announcements:

The new Migration Assistant for SQL databases simplify moving SQL Server and Azure SQL workloads into Fabric. Designed for SQL developers, it imports schema via DACPACs, identifies compatibility issues, and provides clear, actionable guidance before migration. Built-in assessment and data copy workflows help teams move from evaluation to cutover with less manual effort, preserving existing SQL skills while accelerating time to value on Fabric’s unified analytics platform.  Ready to simplify your SQL migration journey? We will begin rolling this out in the coming weeks, and it will soon be accessible through the Fabric portal.

Click through for more things that are currently in place, including several items that are now GA.

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What’s New in OneLake

Published 2026-03-20 by Kevin Feasel

Josh Caplan provides an update:

With shortcuts and mirroring in OneLake, you get zero-copy, zero-ETL capabilities to connect your multi-cloud data estate. Whether your data sits in Azure, AWS, Google Cloud, or Oracle, on-premises, or across platforms like SAP, Dataverse, Snowflake, and Azure Databricks, you can connect it to OneLake without data movement or duplication. No more sprawling ETL pipelines. No more out-of-date copies. No more data silos.

Today, we’re expanding mirroring to now include SharePoint lists (Preview) and adding mirroring via shortcuts for Azure Monitor and Dremio (Preview). We are also releasing mirroring for Oracle and SAP Datasphere into general availability. Beyond these core mirroring capabilities, we are now introducing extended capabilities in mirroring designed to help you operationalize mirrored sources at scale. These capabilities include Change Data Feed (CDF) and the ability to create views on top of mirrored data, starting with Snowflake and will be offered as a paid option.

Click through for more of what came out of FabCon.

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Materialized Lake Views now GA in Microsoft Fabric

Published 2026-03-19 by Kevin Feasel

Balaji Sankaran makes an announcement:

Since introducing MLVs (Preview) at Build 2025, data engineers have used them to replace hand-built ETL pipelines with a few declarative Spark SQL statements, and their feedback directly shaped this release.

This update closes the most important gaps since reaching preview and makes MLVs production-ready at scale. With multi-schedule support, broader incremental refresh, PySpark authoring, in-place updates, and stronger data quality controls, teams can now build, run, and evolve medallion pipelines with far less operational overhead.

Click through to see what’s changed since the preview.

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