An AWS data lake is a solution for centralizing, organizing, and storing data at scale in the cloud, typically using Amazon Simple Storage Service (S3) as a storage backing. It provides bulk storage for structured, semi-structured, and unstructured data, allowing for data analytics at scale. To optimize an AWS data lake, it's essential to implement best practices such as capturing and storing raw data in its source format, leveraging S3 storage classes to optimize costs, implementing data lifecycle policies, utilizing Amazon S3 object tagging, managing objects at scale with S3 batch operations, combining small files to reduce API costs, managing metadata with a data catalog, querying and transforming data directly in Amazon S3 buckets, compressing data to maximize retention and reduce storage costs, and simplifying the architecture with a SaaS cloud data platform. By following these best practices, organizations can configure and operate an AWS data lake solution that empowers them to extract valuable insights from their data faster than ever before.