Data Warehouse vs Data Lake vs Data Lakehouse

Compare data warehouses, data lakes, and data lakehouses.

Data Warehouse

Stores cleaned, structured data optimized for analytical queries (OLAP).

Examples: Snowflake, BigQuery, Redshift

Pros: High query performance, strong governance. Cons: No unstructured data support, high cost, less flexible.

Data Lake

Stores all data in raw format (Parquet, CSV, JSON, video) with Schema-on-Read (structure defined at query time).

Examples: S3 + Athena, Azure Data Lake Storage

Pros: Cheap storage, preserves all raw data. Cons: Becomes a "data swamp," hard to govern, slow queries.

Data Lakehouse

Combines both: stores data in low-cost object storage (S3) with an added layer providing ACID transactions, schema management, and performance optimization.

Examples: Delta Lake (Databricks), Apache Iceberg, Apache Hudi

Features: ACID transactions, Time Travel (query historical versions), Schema Evolution — at data lake cost.