What is the difference between a data warehouse, a data lake, and a lakehouse?

Answer

A data warehouse stores structured, modeled data optimized for fast SQL analytics, typically loaded through schema-on-write pipelines; examples include Snowflake, BigQuery, and Redshift. A data lake stores raw data of any type (structured, semi-structured, or unstructured) cheaply in object storage with schema-on-read, which is flexible but can become a disorganized 'data swamp.' A lakehouse combines both: it keeps data in open formats on cheap object storage while adding warehouse-style features such as ACID transactions, schema enforcement, and performance via table formats like Delta Lake, Apache Iceberg, or Hudi. The lakehouse aims to serve both data science and BI from one copy of the data.