A data lakehouse is a modern data architecture that unifies data lakes and data warehouses, combining the scalability and flexibility of storing raw, structured, and unstructured data with the performance and reliability of traditional warehouse features like ACID transactions, schema enforcement, and governance. Emerging as a response to the limitations of siloed systems, the lakehouse eliminates redundant ETL processes and allows both BI and ML workloads to operate on a single copy of data. The architecture relies on open table formats (Apache Iceberg, Delta Lake, Apache Hudi) that transform object storage into queryable, versioned, transactional tables—enabling practitioners to run SQL analytics, real-time streaming, and ML feature engineering all in one platform without data duplication.