The 'batch' postfix in functions like transform_batch() indicates that the operation is performed on chunks of the DataFrame or Series, rather than the entire dataset at once. This approach leverages Spark's distributed processing capabilities to efficiently handle large datasets by dividing them into smaller, manageable pieces. Chunk-based operations can improve performance and memory efficiency, allowing for scalable and parallel processing across a Spark cluster. For example, applying a function to square values in each chunk of a DataFrame can be done efficiently with transform_batch(), showcasing the benefits of chunk-based operations in terms of scalability, parallelism, and resource management.