The most significant data engineering strategy to emerge in the last several decades is the data lake. I’ll explore modern data lake design theory and practice, including its evolution to the lakehouse strategy. I’ll present advanced serialization formats and open table formats like Apache Iceberg that can support transactions, upserts, and merges in a data lake. I’ll highlight the strategic investments by AWS to offer managed Iceberg tables in S3 via a feature called S3 Tables buckets. Throughout the chapter, I’ll highlight opportunities to support data lake development with generative AI and how data lakes support generative AI use cases. By the end of the chapter, you should be able to explain what a data lake is, how it originated, and why it became a dominant data strategy. You should also be able to identify the primary components of a data lake and its transactional derivative, the lakehouse. You should understand how to map out and define data quality stages or phases. Finally, you should know which aspects of data lake design are the most impacted by generative AI.