A data lakehouse is a modern data architecture that combines the features of both data lakes and data warehouses. While a data warehouse is a traditional data storage and processing solution, a data lakehouse aims to bridge the gap between these two concepts. Here are some key differences between a data lakehouse and a data warehouse:

1. Data types and formats: Data warehouses are designed primarily for structured data, such as data stored in relational databases. In contrast, data lakehouses can handle structured, semi-structured, and unstructured data, making them suitable for diverse data sources and formats.
2. Storage: Data warehouses typically use proprietary storage formats and are optimized for specific database management systems. Data lakehouses, on the other hand, use open storage formats like Parquet, Delta Lake, or Apache Iceberg, which enables greater flexibility and interoperability with various tools and platforms.
3. Schema: Data warehouses follow a schema-on-write approach, which requires data to be cleaned, transformed, and structured before being ingested. Data lakehouses support both schema-on-write and schema-on-read approaches, enabling greater flexibility in data processing and allowing raw data to be ingested and transformed as needed.
4. Scalability: Data warehouses may have limitations when it comes to horizontal scalability, which can hinder performance when dealing with large volumes of data. Data lakehouses are designed to scale horizontally, making them more suitable for handling massive volumes of data efficiently.
5. Cost: Data warehouses often involve higher upfront costs and ongoing maintenance expenses due to proprietary technology and dedicated infrastructure requirements. Data lakehouses leverage cloud storage and compute resources, offering a more cost-efficient solution with a pay-as-you-go model.
6. Data governance and management: Data lakehouses incorporate data management and governance features typically associated with data warehouses, such as schema enforcement, versioning, and transaction support. These features ensure data integrity, consistency, and security, which may be lacking in traditional data lakes.

In summary, a data lakehouse combines the best features of data lakes and data warehouses to create a more versatile, scalable, and cost-effective solution for managing and processing diverse types of data. It is designed to address the limitations of traditional data warehouses while maintaining the benefits of data lakes.