A Fine Slice Of SQL Server
kamurray has a big list of updates:
This month’s update brings a broad set of new capabilities across Microsoft Fabric, spanning the platform experience, Data Engineering, Data Science, Data Warehouse, and Real-Time Intelligence. Read on to learn about improvements to the Fabric experience, deeper VS Code integration, enhanced notebook resiliency, expanded machine learning and governance features, and new real-time data processing capabilities.
Click through to see what’s new.
Leave a CommentNicky van Vroenhoven gives the standard consulting answer:
I think I have gotten this question at least five or six times in the last few months, and with Translytical Task Flows reaching GA in the March 2026 Power BI update, I expect it to come up even more. So let me write it down once and for all.
The question usually sounds something like: “We want users to be able to add comments or update values in their Power BI report. Should we use Power Apps or this new Translytical Task Flows thing?”
My honest answer is: it depends 😆, but the decision is simpler than you might think.
Click through for the decision criteria.
Leave a CommentMeagan Longoria wants information:
Materialized lake views (MLVs) in Microsoft Fabric are an effective way to implement medallion architecture declaratively, but once you have a pipeline of MLVs in production, you need visibility into whether they’re current. Fabric’s MLV management area gives you a visual lineage and refresh history, but if you want to build automated alerting, logging, or custom tooling, you need to get that information programmatically. This post walks through one way to do that, using a small demo lakehouse built entirely in a Fabric notebook.
Click through for that demonstration.
Leave a CommentVaibhav Shrivastava makes an announcement:
The SQL operator was first introduced in preview to give customers an early look at a code‑first transformation experience in Fabric Eventstreams. During preview, customers used the SQL operator to simplify real-time pipelines, consolidate transformation logic, and unlock advanced scenarios using familiar SQL semantics. Feedback from this phase directly shaped the GA release—driving improvements across multi‑destination support, event‑time processing, testing capabilities, and overall production readiness.
Building on this preview momentum, we’ve reached the next milestone for SQL operator in Fabric Eventstreams, a powerful, code‑first way to transform and route data across Fabric’s Real-Time Intelligence experiences. This complements Eventstream’s no-code capabilities, giving engineers the flexibility to choose the right abstraction for their scenarios.
With this release, you can define transformation and routing logic once using familiar SQL semantics and seamlessly deliver streaming results to multiple destinations in parallel—all from a single operator.
Read on to see what’s changed since the public preview.
Leave a CommentMike Donnelly explains a term:
The short version: Fabric Ontology is the semantic backbone of Microsoft Fabric. It’s the layer that defines what your business data actually means.
If you’ve ever worked in a large organization, you know the problem. One team calls them “Customers,” another calls them “Clients,” and a third calls them “Account Holders.” Without a shared meaning, your analytics become a mess of conflicting vocabularies. An ontology is just a structured way of naming things and describing their relationships so everyone—and every tool—is using the same dictionary.
I think this is all correct, but I think there’s something more to ontologies than that. At least in the Palantir world, the ontology is not just the business definitions and concepts, but it’s also the actions you can take against that data. In other words, you might have Customers, Clients, and Account Holders. You can add a new customer, update the customer details, send a welcome to a new account holder, etc. Each of these actions is baked into the ontology as well. That’s what makes it different from simply defining where the data lives and how we got it in its current shape.
Leave a CommentNikola Ilic presses the Easy button:
For the longest time, building a medallion architecture in Microsoft Fabric meant stitching together a small orchestra of moving parts: notebooks for the transformations, pipelines for orchestration, schedules for refresh, custom code for data quality checks, and the Monitor Hub for keeping an eye on whether anything actually worked. Every layer worked – until something didn’t, and then you had to figure out which layer broke, why, and which downstream layers got affected along the way.
If you’ve ever tried to debug a silver layer that didn’t update because the bronze notebook failed three hours ago, you know exactly what I’m talking about.
Then, at FabCon Atlanta in March 2026, materialized lake views (MLVs) went generally available. And the story they’re telling is simple: what if your entire medallion pipeline could be a few SELECT statements?
Let me walk you through the whole thing – what they are, how they work, what changed between preview and GA, and where they fit (and where they don’t) in your architecture.
Read on for that walkthrough.
Leave a CommentBack in December the FabricAI.Prompt() M function was released in Fabric Dataflows Gen2. Most of the people writing about it at that time, as in this great post by my colleague Sandeep Pawar, focused on calling this function for each row in a table – something that the UI in the editor makes easy. However the FabricAI.Prompt() function itself is a lot more flexible. You can use it to summarise whole tables of data as I showed here; you can also use it to generate sample data. This is similar to what I blogged about here where I got Copilot to generate M code that returned sample data but using FabricAI.Prompt() is maybe a bit simpler.
Click through to see how.
Leave a CommentRuixin Xu announces a feature now generally available:
Cross-workspace logging works through the synapseml-mlflow package, which provides a Fabric-compatible MLflow tracking plugin. The core idea is simple: set the MLFLOW_TRACKING_URI* to point at your target workspace and use standard MLflow commands. Your experiments, metrics, parameters, and registered models land in the workspace you choose — not just the one you’re running in.
Read on for the full announcement.
Leave a CommentXu Jiang and Nikola Zagorac take a look at Change Event Streaming:
Traditionally, Change Data Capture (CDC) has been the go-to mechanism for tracking SQL Server data changes. However, CDC relies on polling-based capture with intermediate change tables, introducing latency and operational overhead, such as managing polling, offsets, and replaying windows in connector. Change Event Streaming (CES), introduced in SQL Server 2025, Azure SQL Database, and Azure SQL Managed Instance, takes a fundamentally different approach: it pushes data change events directly from the database engine to external streaming platforms in real time. Built on the CloudEvents specification, CES delivers structured JSON messages with the operation type and full row data – eliminating intermediate tables and reducing end-to-end latency to near zero.
Click through for more information, though Change Event Streaming is still officially a preview feature in SQL Server 2025
Leave a CommentAlex Lin looks at network security features:
Eventstream in Fabric Real-Time Intelligence stream data from both inside and outside the Fabric platform. When your external sources sit behind firewalls or in private networks, choosing the right network security feature is essential. This post breaks down the available options in Eventstream and helps you determine which one fits your scenario.
Click through for more information.
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