Fabric Plan - End to End workflow
Hi, I'm currently evaluating Fabric Planning and would like to understand the intended workflow better. As I understand it, the flow could look something like this: I create my plan (e.g. a budget or forecast) in Fabric Planning, write the data back to Fabric, and then use it in Power BI reports alongside actuals. Is that how Microsoft intends the product to be used? If not, could you describe the recommended end-to-end workflow, from creating a plan to using the results in reporting? Thanks in advance!
How can AI agents improve decision-making with Microsoft Fabric IQ?
I am exploring how AI agents can work with modern data platforms to help businesses move from traditional reporting toward proactive decision-making. Some possible use cases: AI agents analyzing business data and identifying important trends Automatically generating insights from Fabric data models Triggering workflows based on detected patterns or anomalies Helping teams interact with enterprise data using natural language Combining AI reasoning with governed data sources I would like to understand how the community is approaching AI-powered analytics with Microsoft Fabric IQ. Are teams using AI agents, Copilot experiences, or custom automation workflows on top of Fabric data solutions? What architecture patterns and best practices have you found useful?
Multiple Semantic Models for one Ontology Layer
Hello everyone, When generating an ontology layer, I see that you can generate one from a semantic model. However, I see I can import multiple warehouses and lakehouses as part of an ontology layer if I wanted to start from scratch. Let's say I have four semantic models that each represent an important busines process (finance, sales, inventory, production/procurement). Each semantic model follows a well-defined star schema, and are mature enough to even be considered data products. If I want to eventually create an agent where I access data from both sales and inventory semantic model, would the best practice be to import/use both semantic models in the ontology layer? Or should I create one ontology layer per semantic model, and have my agent consume the information by attaching both ontology layers to said agent? For this example, let's say I want an agent that can see sales history, customer purchasing behavior history from the sales model, and can see inventory of products being ordered fro the inventory model. Back to the original question, what's the best practice ultimately for an agent to consume information from multiple semantic models?Fabric Semantic Model Refresh Error
We are creating semantic model and doing binding through Fabric REST APIs using Python SDK in local VS code. We are using a Service Principal for authentication. That Service Principal already has Admin Access in the workspace which will host the Semantic model. We are able to create the model and also get the relationships established but while getting the connection established with source DB/warehouse/Lakehouse, we are getting the following error message: Data source error: {"error":{"code":"Premium_ASWL_Error","pbi.error":{"code":"Premium_ASWL_Error","parameters":{},"details":[{"code":"Premium_ASWL_Error_Details_Label","detail":{"type":1,"value":"We are unable to refresh the semantic model because the model uses a data connection configured for Workspace Identity authentication, but the workspace has no identity, or the model owner does not have permissions to use the workspace identity. Please configure a workspace identity and make sure the model owner has Contributor (or higher) access to the workspace, or use a different authentication method."}}],"exceptionCulprit":1}}} Do we just need to enable Workspace Identity or anything more is required as well?
