Data lakes differ from traditional data warehouses primarily in their data structure and purpose. A data lake stores a vast amount of raw data in its native format until needed, allowing for greater flexibility and scalability. In contrast, a traditional data warehouse stores processed data in a structured manner, requiring specific schemas upfront. This leads to a more rigid approach that can hinder agility in analytics. Data lakes enable organizations to perform advanced analytics like machine learning on large datasets without the constraints of predefined structures, thereby enhancing insights and data utilization.