I have a data pipeline in one workspace (say Workspace A) which is used to orchestrate a notebook in another workspace (say Worspace B). Workspace A and Workspace B are connected to different Fabric capacities. When I trigger the pipeline fronm Workspace A, I am seeing the capacity is being used from Workspace A and not Workspace B. Is this expected behavior? I have not found any documentation on this.

Hey all, I took the DP-700 today and passed! I will write a proper post with my experiences when I get home later today, but I’ll say it’s definitely a thorough test of your skills! Look forward to now switching to thinking about how best to teach the content to all of you! 🙌🏽

Hey everyone - yesterday the DP-700 exam became generally available, PLUS Microsoft announced 20,000 50% off vouchers to take the exam... so you might be wondering, what's the plan?! With the previous DP-600 exam, we managed to help hundreds, or maybe even thousands of you certified, which is awesome! And for the DP-700, I have plans to go even bigger! Therefore, this is a summary of what's planned: 📽️ Like with the DP-600 exam, I will be producing videos for the YouTube channel to walk you through each section of the DP-700 study guide - these videos will also be added to the Classroom here in the LMF community, and discussion posts made for conversations about the topics! I hope to start releasing these in the next few weeks. As well as the videos, this time, I'll also be hosting a DP-700 bootcamp (from 19th Jan to 30th March) within Fabric Dojo for those that want more support, this includes: - Access to 11 LIVE (online) study sessions held between 19th Jan and 30th March - Access to summary study notes for each section of the exam. - Plus, as a member of Fabric Dojo, you will get access to the 90+ hands-on tutorials and projects in the Dojo, 60 of which are relevant for DP-700 learning. Practical experience really makes a difference when you're sitting the exam. This is what I spent 6 months working on last year! It's nearly 30 hours of exclusive content and hands-on learning related to Fabric Data Engineering and Analytics Engineering.

There's a section here for Fabric wins, but could we have one for losses? Or misses? ha! I'm only half kidding. Wanted to share something that I have learned recently about Fabric, specifically related to deployment pipelines. We use deployment pipelines in most of our client projects. Typically, we follow dev, test, prod, but sometimes we use dev, test, qa, prod. We have really enjoyed and benefited from deployment pipelines the past several years before using Fabric. It works wonderfully with semantic models and reports. However, with Fabric data engineering items, not so much. Though, I'm told there's hope that there will be improvements soon. Things I've learned about fabric items when using deployment pipelines 1. dataflows don't work with deployment pipelines. You must manually export the json and import it in your next workspace. This means any changes you make in dev that are ready to move to test have to manually moved over. 2. data pipeline connections have to manually configured whenever you deploy from one workspace to another. There is not currently parameterized connections in pipelines, so you can't setup deployment rules to switch the connection like you can for other items (such as the lakehouse a semantic model points to or a parameterized connection string in a semantic model) 3. SQL tables will migrate from a data warehouse, but the data won't come with them. -- data will need to be manually loaded (or use a notebook or some other automation to pull in the data) 4. Similarly, manually loaded tables in a lakehouse don't get copied over. They will need to be manually created in the new lakehouse (tables created from notebooks can be set to auto create in the new workspace provided that you copy your notebook over as well) 5. shortcuts don't work with deployment pipelines -- they also need to be created manually I'm told that parameterization of data pipeline connections is coming in Q1 2025 and that dataflows are also set to start working in deployment pipelines in Q1 (though they were originally supposed to be available in Q4 2024).

Just wanted to wish everyone here a fantastic 2025 - let's make it a great year 😀💪 Do you have any goals for the year? Share them below for some accountability! 👇