Spark schemas

Spark only supports schema on read and write, so you will likely find it necessary to define your schema manually. Spark has many data types. Once you know how to represent schemas, it becomes rather easy to create data structures.

One thing to keep in mind is that when you define a schema in Spark, you must also set its nullability. When a column is allowed to have nulls, then we can set it to True; by doing this, when a Null or empty field is present, no errors will be thrown by Spark. When we define a Struct field, we set three main components: the name, the data type, and the nullibility. When we set the nullability to False, Spark will throw an error when data is added to the DataFrame. It can be useful to limit nulls when defining the schema but keep in mind that throwing an error isn’t always the ideal reaction at every stage of a data pipeline.

When working with data pipelines, the discussion about dynamic schema and static schema will often come...