Why is schema evolution important in modern data lakes?

Example Concept: Schema evolution refers to the capability of a data storage system to accommodate changes in the data schema without requiring a complete overhaul of the existing data. In data lakes, this means supporting operations like adding new fields, modifying existing fields, or even removing fields, all while maintaining backward compatibility. This flexibility is often managed through metadata layers and tools like Apache Hive, Apache Iceberg, or Delta Lake, which provide mechanisms to handle schema changes gracefully, ensuring that data integrity and accessibility are preserved.