{
  "id": "fabric-data-engineering",
  "name": "Fabric Data Engineering",
  "type": "skill",
  "provider": "microsoft",
  "harnesses": [
    "codex",
    "claude-code",
    "cursor",
    "gemini",
    "kiro",
    "other"
  ],
  "summary": "Review Microsoft Fabric data engineering artifacts: Lakehouse and OneLake design, medallion (bronze/silver/gold) architecture, Spark notebooks and Spark job definitions, Data pipelines and Dataflows Gen2, Delta/Parquet storage and OneLake shortcuts, Real-Time Intelligence (eventstreams, KQL databases, eventhouse), Direct Lake semantic-model source design, ingestion and orchestration patterns, Capacity Unit (CU) efficiency, and Git integration and deployment pipelines for engineering items. Use to fix brittle pipelines, poor medallion layering, capacity overruns, and fragile ingestion patterns. Static review only; production pipeline runs, capacity changes, and deployment-pipeline promotions are live-guard gated.",
  "source_type": "original",
  "official_docs": [
    "https://learn.microsoft.com/fabric/onelake/onelake-medallion-lakehouse-architecture",
    "https://learn.microsoft.com/fabric/data-engineering/tutorial-lakehouse-introduction",
    "https://learn.microsoft.com/fabric/data-factory/dataflows-gen2-overview",
    "https://learn.microsoft.com/fabric/real-time-intelligence/event-streams/overview",
    "https://learn.microsoft.com/credentials/certifications/resources/study-guides/dp-700"
  ],
  "security_notes": "Static review only. Never execute production Spark notebooks, trigger production pipeline runs, promote deployment-pipeline stages, resize or pause Fabric capacity, or modify OneLake folder-level or item-level access controls without explicit human approval, blast-radius assessment, and a documented rollback path; these are live-guard gated and must be escalated to a Fabric administrator. Do not ask for credentials, tenant IDs, workspace URLs, connection strings, or customer data. Treat unpartitioned bronze tables, missing Delta optimization (V-Order, Liquid Clustering), unbounded Spark jobs, unchecked CU consumption, missing incremental-load logic, and eventstream destinations without error handling as reliability and capacity risks until reviewed. Label every finding as documented artifact, user-provided sanitized evidence, documentation-based, or inference.",
  "last_verified": "2026-06-17",
  "path": "skills/microsoft/fabric-data-engineering",
  "author": "github: VincentChuWaiChow",
  "version": "0.1.0",
  "category": "data",
  "companion_agents": ["fabric-data-engineering-agent"]
}