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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."
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Written byAlvin Varughese
Founder•18 professional certifications