A data lakehouse is a unified architecture that combines the strengths of data warehouses and data lakes. You store diverse data types cost-effectively while retaining reliability, performance, and governance for analytics and AI.
How it works (five layers)
- Ingestion — batch and streaming sources land in open formats
- Storage — cloud object storage scales independently of compute
- Metadata — table formats enable ACID, versioning, and schema evolution
- API / compute — engines query the same data for SQL, streaming, and ML
- Consumption — BI, notebooks, and applications share one foundation
Why teams adopt lakehouses
- Less duplicate infrastructure
- Faster access to a single source of truth
- Independent scale of storage and compute
- Open formats that reduce lock-in
If you are maintaining separate lakes and warehouses — or building greenfield — the lakehouse model is worth serious consideration.
