Schema management is one of the most overlooked yet critical aspects of building reliable data pipelines. In a fast-moving environment, schemas rarely remain static: new fields are added, data types evolve, and nested structures become more complex. Relying on hard-coded schemas within Spark jobs may seem convenient at first, but it quickly turns into a