What is a common use of higher-order functions in Spark SQL?

Higher-order functions in Spark SQL are commonly used to apply complex transformations to datasets. These functions allow users to pass in functions as parameters or return functions as results, enabling more sophisticated manipulations of data structures.

For example, a higher-order function can be utilized to process arrays or maps within a dataset, enabling operations such as filtering, transforming, and aggregating data in a more dynamic and functional programming style. This capability is particularly powerful when working with nested data types or when requiring operations across multiple columns or complex data structures.

Using higher-order functions streamlines the process of performing complex data transformations, as they provide a way to write more concise and expressive code compared to traditional SQL functions, which might require multiple individual statements or joins to achieve similar outcomes.