What characterizes a Dynamic Frame in AWS Glue?

A Dynamic Frame in AWS Glue is specifically designed for handling data that is often semi-structured. This characteristic sets it apart from traditional structured data processing. Dynamic Frames provide built-in capabilities that help manage the challenges associated with semi-structured data formats like JSON or XML, including handling varying schemas. This adaptability is essential when working with diverse data sources and formats, making it easier for analysts and data engineers to work with data that may not fit neatly into a tabular format.

Dynamic Frames also support transformations that are necessary for preparing data for analytics or machine learning, making them a versatile choice for ETL (Extract, Transform, Load) processes. Their ability to manage schema evolution seamlessly, as well as their inherent support for complex data types, further reinforces their suitability for semi-structured data manipulation.

The other options do not align with the primary advantages and functions of Dynamic Frames. They might unduly restrict the understanding of these data structures or misrepresent their intended purpose in AWS Glue workflows.