The structured data lake concept involves automatically generating and adjusting a schema on write, enabling a robust and clean environment downstream. This approach aims to save time during creation, maintenance, and recovery by automating data structuring processes. The current solution for managing unstructured data is to structure the data first with some kind of automation, which reduces maintenance, simplifies the data, enables automation, facilitates value extraction, and makes it more scalable. A new library called dlt offers schema evolution capabilities, enabling organizations to impose structure on data as it's loaded into the data lake, ensuring consistency and quality, improving performance, ease of use, and data governance. By adopting a 'structure first' approach with dlt, organizations can effectively manage unstructured data in common destinations, optimizing for both flexibility and control.