Data lakehouses are a new architectural pattern that combines the scalability of a data lake with the performance and structure of a data warehouse, allowing organizations to store structured, semi-structured, and unstructured data in its raw form while providing tools for data governance, security, and query optimization. Data lakehouses offer nearly unlimited scale, separation of compute and storage, ACID transaction support, data governance and management features such as snapshots and time travel, fine-grained access control, and auditing. They provide real-time analytics capabilities, reduced costs by streamlining data management practices, simplified architecture and unified data management, and can be built using pre-built services or open source tools like Apache Spark and Presto. However, they also come with challenges such as implementation complexity, data governance and security, vendor lock-in, variable query performance, and the need for automated performance optimization and improved semantic layer capabilities.