What is a key feature of data lakehouse architecture?

The essence of data lakehouse architecture lies in its ability to merge the best aspects of both data lakes and data warehouses. This architecture accommodates a diverse range of data types, including structured, semi-structured, and unstructured data. By doing so, it enables advanced analytical capabilities and provides a unified platform for data storage, processing, and analysis, facilitating better data accessibility and insights across various workloads.

This blend of features allows organizations to leverage the scalability and flexibility of data lakes alongside the performance and management benefits of data warehouses. Consequently, users can perform complex analytics and reporting on large sets of varied data without having to segregate it into multiple systems.

In contrast, data lakehouse architecture is not limited to just structured data analysis, does not inherently eliminate the need for data transformation, and does not strictly require a cloud-only infrastructure, as it can be implemented in hybrid environments as well.