Data Lake vs Data Warehouse vs Lakehouse The Definitive, Practical Guide
Data architecture breakdown: when each model wins (and when it doesn’t)
Warehouses = governed BI at speed.
Lakes = any data at low cost, ML-friendly, governance is on you.
Lakehouses = ACID tables on object storage, unify BI + ML, but require engineering maturity.
Most orgs run hybrids; the trick is getting governance, cost, and interoperability right.
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