A Fine Slice Of SQL Server
Ruixin 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.
Comments closedXu 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
Comments closedAlex 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.
Comments closedKoen Verbeeck speeds things up:
We are building a large warehouse in Microsoft Fabric using the warehouse. Our biggest fact tables have some performance issues when we are running our analytical queries, and it seems we cannot use indexes in the Fabric Warehouse. Is there some way to improve performance?
Click through to see how you can use clustering to improve the performance of warehousing queries, as well as some of the pre-requisites to make it work.
Comments closedWhen you provision a Microsoft Fabric trial, it spins up at F64 by default. That’s the equivalent of 64 Capacity Units — a solid chunk of compute. For most trial users, this is just the number they see and never think twice about.
But here’s what I just discovered: you can change it.
Click through to see how.
Comments closedNikola Ilic asks, what’s in a name?
My dear Microsoft Fabric friends – if you’ve ever opened a workspace and seen “Lakehouse”, “Lakehouse 1”, “lh_test_v2”, and “NewLakehouse_DELETE_ME” all sitting next to each other, this post is for you
Three weeks into a fresh Fabric tenant, things look great. Twelve weeks in, you’re staring at 47 workspaces, three of them called something like “Test – DO NOT USE”, and nobody on the team can remember which Lakehouse holds the actual production sales data.
I don’t know how Nikola has figured out my naming strategy so well. Click through for a systematic attempt to standardize naming for Fabric objects.
Comments closedJens Vestergaard performs some checks:
A semantic model is a promise. It promises that the numbers in your reports match the data in your lakehouse. But after enough model changes, renamed columns, new relationships, and tweaked measures, that promise gets harder to verify. I wanted a way to check it programmatically.
This is my second submission to the Fabric Semantic Link Developer Experience Challenge. The first was a DAX unit test harness that compares measures against hardcoded expected values. That works well for known business rules, but it has a limitation: someone has to decide and maintain what the “right” answer is. For a model with hundreds of measures across dozens of filter contexts, that does not scale.
Click through to see what Jens did instead.
Comments closedThis post covers using fab deploy in Azure DevOps for Microsoft Fabric deployments based on YAML pipelines. In addition, this post shows how you can perform initial tests locally and introduces some AI concepts. Plus, this post shares plenty of links and advice.
You can find an example to accompany this post in the ‘create-genworkspace-fabric-cli.yml‘ file my ADO-fabric-cicd-sample Git repository. I also added some AI elements within this Git repository as well. Including the Fabric CLI skills that were announced during FabCon.
Click through to learn more about fab deploy and how the process works.
Comments closedJens Vestergaard wants to know who moved his cheese:
Semantic models do not have version control. Not really. You can store
.pbipfiles in Git, and Tabular Editor gives you a.bimfile you can diff, but neither of those workflows answers the simplest question a team asks after an update cycle: what changed?I do not mean “which file was touched.” I mean: which measures were modified, which columns were added, which relationships were removed, and what exactly is different in the DAX expression that someone edited last Tuesday. That is the question I kept running into, and the one I built this notebook to answer.
Click through to learn how.
Comments closedKoen Verbeeck troubleshoots an issue:
A short blog post about an issue with Fabric Mirroring (with Azure SQL DB as the source) that I’ve managed to run into, twice. I’ve set up mirroring by creating a connection using a service principal and this principal has the proper permissions on the source database. Configuring the replication was without issues, and the replication status went from “starting” to “running”. However, no rows were being copied. The tables were all listed in the monitoring pane, but the counters of “rows replicated” remained at zero. There were no errors in the logs (in OneLake) and nothing suspicious was mentioned in the monitoring.
This was a rather pernicious issue. Based on Koen’s explanation, it sounds like there’s no way to know what the actual problem was.
Comments closed