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1.1.3. Fabric Items and Their Relationships
💡 First Principle: Fabric items are the building blocks of analytics solutions—like specialized tools in a workshop. Each tool has a specific purpose: you wouldn't use a hammer to measure length, and you wouldn't use a lakehouse for real-time millisecond queries. Understanding which tool to reach for determines whether your solution succeeds or struggles.
Comparative Table: Fabric Item Types
| Item Type | Primary Purpose | Storage Location | Query Language |
|---|---|---|---|
| Lakehouse | Store and process big data | OneLake (Delta tables + Files) | Spark SQL, PySpark |
| Data Warehouse | Structured analytics | OneLake (Delta tables) | T-SQL |
| KQL Database | Real-time analytics | OneLake | KQL |
| Eventstream | Real-time data ingestion | Transient (routes to destinations) | Visual editor |
| Data Pipeline | Orchestration | Metadata only | Visual + expressions |
| Dataflow Gen2 | Low-code ETL | OneLake staging | Power Query (M) |
| Notebook | Code-based processing | OneLake (output) | PySpark, Spark SQL |
Visual: Item Relationships in a Typical Architecture
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Written byAlvin Varughese•15 professional certifications