Lakehouse and data warehouse are two approaches to analytical infrastructure. Lakehouse offers flexibility and lower costs, warehouse performance and simplicity. When to choose which?
Data Warehouse¶
- Managed service — Snowflake, BigQuery, Redshift
- Optimized performance — sub-second queries out of the box
- Simplicity — SQL, no infrastructure
- Costs — compute + storage coupled (more expensive)
Lakehouse¶
- Open source — Spark + Delta Lake/Iceberg
- Flexibility — multi-engine, multi-format
- Decoupled compute/storage — cheaper scale
- Complexity — more components to manage
Decision Criteria¶
# Lakehouse vs Data Warehouse — When to Choose Which Approach
# - Small/medium team without infra engineers
# - Primarily SQL workloads
# - Quick start is priority
# - Budget for managed service
# Choose Lakehouse when:
# - Large team with infra experience
# - Mix SQL + ML + streaming
# - Cost optimization is priority
# - Multi-engine requirement
# - Vendor lock-in is concern
Hybrid Approach¶
Many organizations combine both — lakehouse for storage and heavy processing, warehouse for BI and ad-hoc queries.
Summary¶
Warehouse for simplicity and quick start. Lakehouse for flexibility and cost optimization. Hybrid approach often best.
lakehousewarehousearchitecturecomparison