Define a schema. Can you use a struct type to define a schema?

A schema is a formal definition of the structure, data types, and constraints of a dataset. It describes what fields exist, their types (e.g., integer, string, date), and any rules such as nullability or value ranges. Using a struct type to define a schema is common in languages like Scala, Java, or frameworks like Apache Spark. For example, in Spark you can write:

case class Person(name: String, age: Int, address: Address)
case class Address(street: String, city: String, zip: String)

Here, Person and Address are structs that form a nested schema. When reading data, Spark infers this schema and enforces type safety, enabling efficient query planning and validation. Structs are especially useful for representing hierarchical data, such as JSON objects or nested tables, and they allow developers to work with strongly typed objects rather than raw key‑value maps.