Hey everyone! This week, it's the IGNITE Conference in San Francisco! As such, Microsoft published their monthly list of Fabric Product Updates a little early this month! 👉🔗Here's the blog post: https://blog.fabric.microsoft.com/en-gb/blog/fabric-november-2025-feature-summary?ft=All 👉📽️ More visual? Here's the YouTube video by Adam: https://www.youtube.com/watch?v=Ym2ADQv1P7Y Some very big announcements hidden in there!! Take a look through and let me know your favorites! Here are some that caught my eye: - Azure DevOps Service Principal & Cross Tenant Support (Generally Available) - SQL database in Fabric (Generally Available) - Optimal Refresh for Materialized Lake Views (Preview) - Connect Data Agents to your Azure Search Index in Microsoft Foundry - Data Warehouse: IDENTITY columns (Preview) - Data Warehouse: Data Clustering (Preview) - Natural Language to Generate and Explain Pipeline Expressions with Copilot (Preview) - Lots more Mirroring source become GA - Connection Parameterization with Variable library for CI/CD (Generally Available) - Copy Job

Hey everyone! We've got a new list of Fabric Product Updates to go through: BLOG: Fabric Product Updates released for August 2025 YOUTUBE: Watch here Take a look through and let me know which ones you're most excited about!

I get it, it's an attractive proposition. Type any technical question into a chat window and get an instant response. Unfortunately (at the moment), it's not quite as simple as that. I think we all know that ChatGPT & other large language models (LLMs) can hallucinate, i.e. confidently giving you answers that: - are wrong - are misleading - were maybe right 6 months ago, but now the answer is irrelevant/ not accurate. With Fabric, they are a few factors that increase the likelihood of hallucinations, that you need to be very aware of: - Fabric is fast moving - things change weekly, monthly. Therefore a feature/ method/ piece of documentation that was used in the last LLM training run 6 months ago, might no longer be relevant, or new features have superseded previous approaches. - Fabric is the evolution of previous Microsoft data products. This is good in some ways, but catastrophic for LLMs (and learners relying on LLMs). There is vastly more training data out on the internet for Azure Data Factory, for example, than Fabric Data Factory. Or Azure Synapse Data Engineering over Fabric Data Engineering. And yes there are similarities for how the old tools work vs the new tools, but you need to be super careful that the LLM generates a response for FABRIC Data Pipelines, rather than Azure Data Factory pipelines, for example. Or generates Fabric Data Warehouse compliant T-SQL code, rather than Azure SQL code. This is very difficult, unless you have knowledge of how both products work (which most learners/ beginners don't!). I'm not saying don't use LLMs for studying, just that you need to be super careful. I can think of two use cases that are lower risk, using LLM+Fabric for Spark syntax & KQL syntax generation. That's because Spark and KQL are very mature ecosystems, with lots of training data on the internet, and their syntax won't change too much over the months and years. Fabric Data Warehouse T-SQL code generation is more tricky/ risky because the way the Fabric Data Warehouse works is quite different to a conventional SQL Server (which is what most of the training data will be based on).

Reza posted it, and Kim Manis likes it, so I'm happy to share it here too 😆 GO GO GO

If you're working with D365 or Dataverse, and trying to get that data into Fabric, this article by Tommy Skaue, Senior FastTrack Solution Architect at Microsoft, will be useful for you! Article link: 🔗👉 https://www.linkedin.com/pulse/dynamics-365-fabric-how-data-changes-made-available-tommy-skaue-f3dwf/ I know there's a few of you working with this D365/ Fabric integration - how are you finding it?