Date slicer
Hi, I need some help with Power BI. I want to use a Between Date Slicer and display the dates in this format: DD-MMM-YYYY Example: 01-Jan-2020 I don’t want to use the default dropdown/date format. I specifically want the Between slicer with two date inputs (From Date and To Date), but I need the displayed dates to appear as 01-Jan-2020 instead of the default format. Could you please guide me on how to achieve this? If the standard Power BI slicer does not support this format, is there any custom visual or alternative solution that can provide the same Between Date functionality with the DD-MMM-YYYY format? Thanks!
How can AI agents improve Microsoft Fabric Data Factory pipeline automation?
I am exploring how AI agents can help make data pipeline development and monitoring more efficient in Microsoft Fabric Data Factory. Some potential use cases: Automatically creating pipeline workflows based on business requirements Detecting pipeline failures and suggesting fixes Optimizing data movement and transformation steps Monitoring data quality and identifying anomalies Triggering automated actions through APIs or external tools I would like to understand how teams are currently combining AI agents with Fabric Data Factory. Are you using: AI-assisted pipeline generation? Automated error analysis and recovery workflows? Fabric notebooks with AI models for data operations? Custom agents connected with Data Factory pipelines? Would appreciate insights, real-world examples, or architecture patterns from developers working with Fabric Data Factory automation.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?
Copy Job Not Saving
I want to know how everyone is dealing with this. I have a Copy Job that brings over 30+ tables from one lakehouse to another and has column mappings. I find that when I try to edit the mapping of one table after running the Copy Job and then hitting the Apply button, I receive a validation model error message even though I only edited a single table and then the mappings of the other tables disappear and I have to redo the mappings for all 30+ tables. If I try to add another table from the source, all my mappings from the previous sources will disappear as well and I have to redo it. I was hoping that I could hold onto the JSON code and paste in the old mappings but there's no way to edit the Copy Job JSON file only to view it. How is everyone editing their Copy Jobs?
Fabric Data Factory system variable pipeline().triggertype
Hello I would be grateful if someone from Microsoft would clarify whether, for Fabric Pipelines, the system variable pipeline.triggertype() is officially supported. Our pipeline is set up to be triggered by a schedule. At the moment, on pipeline run, when we pass the value of the system variable pipeline.triggertype() through to our own logging tables, it returns the value of either Manual or 1. This is different to what is stated in the pipeline development user interface, which shows that it passes through value of either Manual or Scheduler. Also in the documentation below re pipeline scope variables for Fabric data factory, no mention is made of the system variable pipeline.TriggerType() https://learn.microsoft.com/en-us/fabric/data-factory/expression-language#pipeline-scope-variables Please clarify if the values of Manual and 1 currently being passed through are a reliable means to determine whether the pipeline was a manual or scheduled trigger - or will these values be subject to change? If not reliable, I will have to develop a manual work around. Many thanks for your help in anticipation.
Sharepoint lists mirroring issue
Hello, We currently face an issue with sharepoint lists mirroring in microsoft fabric and we have no clue what can happen because the error message is pretty useless : In fact, we are mirroring more than 20 lists but only half of them succeed. The others failed with the following error : Internal system error occurred. ArtifactId: daa35143-...-5aa6accfec68, SequenceNumber: 9 I have linked an eventhouse but the error message is still the same, no more information: Internal system error occurred. ArtifactId: daa35143-...-5aa6accfec68, SequenceNumber: 9 How can I have more information about the issue ? Thank you very much for your help, RegardsFabric Business Events: what delivery guarantees and replay pattern should we design for?
Hi all, I am testing the newer Business Events capability in Fabric Real-Time Intelligence and trying to understand what reliability assumptions should be made for a production design. The pattern I am looking at is roughly: Eventstream → Business Event → Activator → downstream action / User Data Function with Eventhouse enabled so the published business events are also retained for historical analysis. The current documentation explains the publisher/consumer model and shows how Eventstream can publish a governed business event that Activator then consumes. What I have not been able to find clearly documented is the delivery contract between the published business event and its consumers. A few things I am trying to clarify: If an Activator consumer or downstream action is temporarily unavailable, does Fabric retry delivery of the business event? Should consumers assume at-least-once delivery and therefore be designed to handle duplicate events, or is a different delivery model used? Is event ordering guaranteed in any scope, for example for events from the same Eventstream publisher? Since published business events can also be retained automatically in Eventhouse, is that retained history intended to support replay/reprocessing after a consumer outage, or is it primarily an analytical record and replay would need to be implemented separately? Are there documented retry or delivery-retention windows that should be considered when designing an operational workflow? I am mainly trying to understand what a resilient production pattern should look like when the business event triggers something with side effects, where processing the same event twice or silently missing an event would matter. Would you generally make the downstream consumer idempotent and treat Eventhouse as an audit/recovery store, or is there a more Fabric-native pattern for this? Interested to hear how others are approaching this with Business Events and Activator.
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!
