Data lakes and data warehouses are both essential components of data management but serve different purposes. A data lake is designed to store vast amounts of raw, unstructured data in its native format, allowing for flexibility and scalability in data processing. In contrast, a data warehouse is structured to store curated, processed data optimized for analytics and reporting. While data lakes facilitate more exploratory and real-time analytics, data warehouses provide reliable insights about historical performance. The choice between the two often relates to an organization’s analytical needs and the type of data involved.