Reviewed by Aditya Kumar · Last reviewed 2026-03-24
WAREHOUSE (Snowflake, BigQuery, Redshift): Optimized for SQL analytics; schema-on-write; excellent query performance; structured data. LAKEHOUSE (Databricks, Delta, Iceberg): Combines lake storage (S3) with ACID, schema enforcement, batch + streaming; supports semi/unstructured....
Red Flag: Picking one and dismissing the other. Pro-Move: 'We use Delta as the single source; Snowflake as a consumption layer for BI—best of both; migrated cold data to Iceberg and cut storage cost 60%.'
WAREHOUSE (Snowflake, BigQuery, Redshift): Optimized for SQL analytics; schema-on-write; excellent query performance; structured data. LAKEHOUSE (Databricks, Delta, Iceberg): Combines lake storage (S3) with ACID, schema enforcement, batch + streaming; supports semi/unstructured. WHY THE DISTINCTION: Warehouse = governed, high-performance analytics; Lakehouse = diverse data types, ML workloads, scale storage separately from compute. SCALABILITY: Warehouse scales compute+storage together; Lakehouse decouples—storage cheap, compute on-demand. COST: Warehouse has higher $/TB for cold data; Lakehouse uses object storage (cheap) + compute when needed. TRADE-OFF: Many orgs use both—Lakehouse as raw/curated, Warehouse or Lakehouse SQL for consumption.
Red Flag: Picking one and dismissing the other. Pro-Move: 'We use Delta as the single source; Snowflake as a consumption layer for BI—best of both; migrated cold data to Iceberg and cut storage cost 60%.'
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