Data lake architectures represent a fundamental departure from traditional data warehousing approaches, embracing schema-on-read principles and polyglot storage strategies that accommodate the velocity, variety, and volume characteristics of modern data ecosystems. Unlike data warehouses that require upfront schema definition and ETL processes to conform data to predefined structures, data lakes preserve raw data in its native format while providing flexible analysis capabilities that adapt to evolving analytical requirements. AWS provides a comprehensive suite of services that enable sophisticated data lake implementations while managing the operational complexity traditionally associated with big data platforms.