A data lake is a centralized repository that stores vast quantities of raw data in its native format, accommodating structured, semi-structured, and unstructured data for flexible, large-scale analytics and machine learning. While powerful, data lakes can lack the data management, reliability, and performance features of a traditional data warehouse, sometimes leading to disorganized "data swamps." The data lakehouse is a modern architectural paradigm that evolves this concept by combining the low-cost, flexible storage of a data lake with the robust data structures and management features of a data warehouse, such as ACID transactions, schema enforcement, and indexing. This hybrid approach aims to create a single, unified platform that can efficiently support both business intelligence (BI) and data science workloads directly on the same data, eliminating data silos and redundancy.