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1.2.2. Storage-Compute Separation
💡 First Principle: Traditional databases couple storage and compute—if you need more query power, you scale up the whole system, including storage you may not need. Modern data platforms separate them: storage scales independently of compute, and multiple compute engines can read the same data. This is why a Lakehouse, Data Warehouse, and KQL Database in Fabric can all access the same OneLake data.
The implications for cost and architecture:
| Aspect | Coupled | Separated |
|---|---|---|
| Scaling | Scale everything together | Scale storage and compute independently |
| Cost | Pay for max(storage, compute) | Pay for each based on usage |
| Flexibility | One compute engine per data | Multiple engines on same data |
| Latency | Data local to compute | Network transfer overhead |
⚠️ Exam Trap: Storage-compute separation introduces latency. Questions about performance optimization might require caching strategies or choosing the right compute engine for the access pattern—not just "add more compute."
Written byAlvin Varughese
Founder•15 professional certifications