The Cloud and Collaboration Systems Practice Test

A data lakehouse blends the flexibility of a data lake with the reliability and speed of a data warehouse. It stores large volumes of diverse data—structured, semi-structured, and unstructured—in a scalable repository, while providing warehouse-like ACID transactions, strong schema management, and fast SQL queries. This lets you run analytics and data science on the same dataset without moving data between separate systems, often by using object storage plus a metadata/transaction layer (like Iceberg, Delta Lake, or Hudi) to support ACID, schema evolution, time travel, and efficient query planning.

The other descriptions miss the mark because they describe either a pure data warehouse with limited data types, a data lake that lacks governance and metadata, or a relational OLTP-focused database, none of which capture the unified, analytics-ready nature of a lakehouse.