The explosion of big data has posed significant challenges for enterprises when it comes to properly analyzing it for insights. The traditional approach of using data warehouses has drawbacks such as ingestion delays, vendor lock-in, and data duplication challenges. However, a new solution has emerged in the form of a data lake, which offers scalable and widely accessible storage of all types of data formats. Despite being relatively new, data lakes are becoming a promising storage and analytics solution. Another approach, known as the “data lakehouse,” combines the advantages of data warehouses and data lakes to provide a look-alike-database processing engine and semantic layer that enables enterprises to query all their data while maintaining the advantages of both methods. Data lakehouses are becoming increasingly adopted in the industry to improve the business value of accumulated data. As more enterprises adopt cloud data lakehouses, they will be able to focus on complex analytics on big datasets and enjoy the cheap, flexible storage capabilities of a cloud data lake while owning their own data and maintaining separate analytical environments for every domain.