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Power BI's Direct Lake mode in Microsoft Fabric enables DAX queries directly on Delta Parquet files in OneLake. It delivers import-level speed with live data access, removing the need for scheduled refreshes and fragile pipelines. Reports load instantly on server capacity, freeing analysts from hours-long waits.
Microsoft Fabric introduces a public REST API to run Power Query M scripts programmatically, connecting to over 100 data sources and integrating with Spark and pipelines. Limited to 90-second execution, it forces efficient transformations, ending reliance on slow, manual desktop refreshes or rigid schedules.
Microsoft Fabric's new feature converts flat files like CSV, JSON, and Parquet into structured tables without building data pipelines or writing code. Users create shortcuts to files in SharePoint, OneDrive, or S3. Fabric syncs changes incrementally and updates table structures automatically. Tables integrate directly with Power BI for faster, simpler reports that refresh in seconds instead of hours.
Power BI Desktop now uses Copilot to generate DAX measures for tasks like period-over-period analysis and top product rankings. Test in DAX Query View to check lineage and logic without risking reports. Limitations persist: it can't fix data model issues, so poor foundations yield wrong results. Shifts focus from syntax to model quality.
Microsoft retires Power BI Premium capacity licenses, replacing them with Fabric capacities. An F64 Fabric license equals old P1 and allows free viewing for all company users without individual Pro licenses. Builders still need Pro to publish, but this eliminates license barriers for report distribution, reducing Excel exports and enabling broader dashboard access.
Microsoft's Fabric updates integrate Apache Airflow for workflow orchestration, Stibo for master data management, and mTLS for secure real-time streaming. These enterprise-focused features target data engineers handling complex pipelines, widening the gap for self-service analysts who should prioritize DAX and clean data models before adopting.
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