Match the following Spark SQL data types with their respective descriptions: array | A column with multiple values map | A column with key-value pairs JSON | A nested column with m... Match the following Spark SQL data types with their respective descriptions: array | A column with multiple values map | A column with key-value pairs JSON | A nested column with multiple fields struct | A column with a JSON object
Understand the Problem
The question is asking to match specific Spark SQL data types with their descriptions. It requires an understanding of the definitions and characteristics of each data type in Spark SQL.
Answer
array | A column with multiple values; map | A column with key-value pairs; JSON | A column with a JSON object; struct | A nested column with multiple fields
The final answer is: array | A column with multiple values; map | A column with key-value pairs; JSON | A column with a JSON object; struct | A nested column with multiple fields
Answer for screen readers
The final answer is: array | A column with multiple values; map | A column with key-value pairs; JSON | A column with a JSON object; struct | A nested column with multiple fields
More Information
In Spark SQL, array and map are used to handle complex data structures within a DataFrame. JSON objects can be parsed into struct types, allowing nested fields to be accessed smoothly.
Sources
- Complex Data Types - Informatica Documentation - docs.informatica.com
- Complex Data Types - Spark 3.5.3 Documentation - spark.apache.org
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