Data modeling is not important in a Lakehouse architecture as it is in a traditional data warehouse, because Lakehouse inherently supports schema-less data storage.

Data modeling is equally important in both Lakehouse architecture and traditional data warehousing, with identical approaches and methodologies applied in both.

Data modeling is more critical in a Lakehouse architecture due to its need to accommodate and efficiently process a mix of structured and unstructured data, enabling advanced analytics and real-time processing capabilities.

Data modeling is less significant in a Lakehouse architecture because it primarily focuses on data storage optimization rather than supporting complex analytical queries.