Infrastructure and licensing costs for adopting Fabric Planning in production (business case)
Hi everyone, We're evaluating Fabric Planning for a client who wants to adopt it in production, and I need to put together a business case with the associated costs. I've already reviewed the role-based session model (Viewer/Stakeholder/Planner, ~37/168/847 CU-hours per 30-day session) and the F64+ capacity requirement for the XMLA endpoint, but I have a few specific questions on infrastructure and licensing that the documentation didn't fully answer: Per-user licensing: besides the capacity (F64+), is any individual license required (Power BI Pro or Premium Per User) for each Viewer/Stakeholder/Planner user, or does the session's CU consumption already cover full access? Production sizing: beyond the technical F64 minimum, what capacity size would you recommend as a practical floor for a real production scenario with several concurrent users, factoring in the ~30% additional infrastructure buffer (OneLake, XMLA API, etc.)? Billing status: is session/role-based billing already in effect (general availability), or is preview usage still free right now? Regional pricing differences: is there any regional price variation we should account for with a client outside the US (Latin America)? Any real-world sizing experience or business-case examples you can share would be much appreciated. Thanks in advance!Best patterns for ingesting dynamic tax & compliance API data into Fabric Lakehouse?
Hi everyone, We are currently migrating our firm's analytical workloads to Microsoft Fabric. A huge part of our business involves tracking dynamic regional tax rates, company registration statuses, and financial compliance data for startups. Currently, our analysts manually pull this data from external regulatory sources and calculators, like this FBR Sales Tax Calculator, and we import it via Excel/CSV into Power BI. We want to fully automate this in Fabric. What is the recommended pattern for hitting external REST APIs on a scheduled basis (daily) to pull dynamic financial data? Should we use Data Factory pipelines with a Web activity to land the JSON directly into a Lakehouse, or is it better to use a Notebook (PySpark) to handle the API pagination and JSON flattening before writing to delta tables? Would appreciate any architecture advice for handling external financial APIs in Fabric! Thanks.Ontology creating relationships using which tables
I today created an ontology based on a lakehouse with the adventureworks tables. i created some elements according to the dimension and fact tables and want to create relationships. as the adventureworks example is a simple warehouse structure there are one-to-many relations possible from the fact table to dimension tables. creating a relationship in the ontology allows me to only choose the mapping based on an existing table. if i choose the fact table, it offers me only the properties of the fact table to be used in specifying how to map to the dimension table. if the names of the properties are the same, everything looks nice, if not, there is no way to specify how to join facts and dimensions. in the adventure works example this happens connection the fact table with order date to the date dimension. How am i supposed to handle that or where are my assumptions wrong, how to use the creation dialog in the ontology?
Undo functionality in Fabric IQ Plan
Hi everyone, I’ve recently started using Fabric IQ Plan which implies which requires working entirely within the platform. One issue I’m running into is that I can’t seem to find any undo functionality - there’s no button for it and even standard shortcuts like Ctrl+Z don't seem to be working. Am I missing something? Is there an undo feature somewhere that I’m not seeing? Or is there another way to revert changes (for example going back n steps)? Thanks in advance! Miriam
Fabric IQ Ontology item for SaaS companies
Hello there Fabricators! I am starting to experiment with the Ontology item which sounds great on paper but I hit obsticles when it comes to defining the entities. The ontology demo that was presented at Microsoft Ignite was great, but as a data analyst in an SaaS company, i have a hard time to define our entities. In the demo example the business was aviation and airport related and the definition of the entities was quite straightforward, but for an SaaS company that sells licenses to customers with a valid from and valid to date the entity modeling is not so straightforward to me (not to mention that we have several types of licenses to several editions of our product and the customer can add or remove licenses at will...). Is there anyone out there who is currently trying to create the ontology for a license based SaaS company? If yes, it would be great to hear your thoughts, strategies and things you learned so far that can help me to create an ontology that can support a Data Agent and deliver trustworthy and consistent answers to the users. Please feel free to ask questions or add your two-cents to the topic 🙂
