Can you explain the difference between a data lakehouse and a data warehouse?

A data warehouse is a structured, schema‑on‑write repository that stores cleaned, curated data in tables optimized for fast analytical queries. It requires a predefined schema and is typically built on relational databases or columnar stores. A data lakehouse blends the raw, unstructured storage of a data lake with the ACID guarantees, schema enforcement, and SQL‑based querying of a data warehouse. It stores data in a lake (often on S3 or ADLS) but uses a transaction log (e.g., Delta Lake, Apache Hudi) to provide versioning, schema evolution, and consistency. This allows users to ingest raw data, apply schema on read, and run analytics with the same engine. In practice, a lakehouse lets you keep the flexibility of a lake while delivering the performance and governance of a warehouse, making it suitable for both exploratory analytics and production reporting.