Data virtualization will become a core component of data lakehouses

Data lakehouses have emerged as a hybrid solution combining the scalability of data lakes with the high-performance query capabilities of data warehouses, offering a cost-effective method for storing and analyzing large datasets. While traditional data warehouses require complex and expensive upfront data cleaning and schema declaration, data lakes allow for a more flexible "store-first, organize-later" approach, albeit with limited querying capabilities. Data lakehouses address these limitations by retaining data in read-optimized formats in the data lake and managing schema and metadata through specialized software, similar to data warehouses. However, the inability to query external data systems limits their potential. Data virtualization technology is poised to become integral to data lakehouses, enabling them to query data across an organization's various systems, including traditional data warehouses, thus offering a unified interface for comprehensive data analysis. Advances in networking and machine learning are enhancing data virtualization capabilities, which are explored in a new book discussing the technical challenges and opportunities within this evolving landscape.