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Lake vs Lakehouse vs Warehouse
Quick compare
| Aspect | Warehouse | Lake | Lakehouse |
|---|
| Data | Structured | Any | Any |
| Schema | On-write | On-read | On-write (Delta/Iceberg) |
| Cost | $$$ | $ | $$ |
| Query | Fast SQL | Slow (Hive) | Fast SQL |
| ML | Limited | Yes | Yes |
| Tools | Snowflake, BQ, Redshift | S3 + Hive | Databricks, Iceberg |
Warehouse strengths
- Mature, BI-friendly.
- ACID transactions.
- Strong governance.
Lake strengths
- Cheap raw storage.
- Multi-format (JSON, parquet, image, video).
- ML training data.
Lakehouse promise
- Best of both — cheap + ACID + SQL + ML.
- Delta Lake (Databricks), Apache Iceberg, Apache Hudi format.
Choosing
- Pure BI + structured data → Warehouse.
- ML + unstructured + flexibility → Lakehouse.
- Cheap archive + occasional query → Lake.
VN reality
- Most start warehouse-first (Snowflake/BigQuery).
- Add lake for ML data (S3 + parquet).
- Lakehouse adoption emerging 2024-2026.
Anti-pattern
- Build lake without clear use case → data swamp.
- Use warehouse for raw event store → expensive.
- Migration warehouse → lakehouse without ROI analysis.