Express: Microsoft Fabric Analytics

Fabric analytics landscape Fabric map: OneLake, lakehouse, warehouse Analytics engineer vs analyst and engineer roles AI-ready data and semantic context for agents Portfolio connection to Copilot experiences Getting and organizing data Data connections and source planning OneLake catalog and Real-Time hub discovery Ingestion vs shortcut vs mirrored data Intake checklist: owner, sensitivity, lineage Transforming and enriching data Dataflows Gen2, notebooks, T-SQL, and KQL Cleansing, merging, aggregation, enrichment Data quality checks before modeling Low-code vs code-based transformation Designing analytical stores Lakehouse, warehouse, and eventhouse patterns Star schema design for lakehouse or warehouse Grain, facts, dimensions, and relationships Store decision matrix by workload and skill Querying and analyzing data SQL, KQL, DAX, and visual query editor roles Reusable query patterns for validation KQL for eventhouse and real-time analytics DAX bridge into semantic model measures Semantic models for BI, Copilot, and agents Semantic model as reusable business logic Import, DirectQuery, Direct Lake, composite Relationships, measures, and calc groups Semantic assets that support Copilot answers Power BI and PL-300 bridge Report and model expectations for Power BI When Fabric Express is enough vs PL-300 Reusable semantic products for authors Adoption and governance implications Security, governance, and sensitivity Workspace roles, permissions, and lineage Sensitivity labels and compliance controls Least-privilege access for AI consumers Governed data exposure for Copilot and agents Analytics lifecycle and deployment Git integration and deployment pipelines Versioning, impact analysis, and notes Reusable assets and ownership model Lifecycle checklist for semantic products AI-ready analytics capstone Design a Fabric data and semantic layer Identify stores, model, governance, lifecycle Document downstream AI-readiness assumptions