What are the advantages of using DynamicFrame over DataFrame in AWS Glue?

DynamicFrame is a higher‑level abstraction built on top of Spark DataFrame that is specifically designed for AWS Glue ETL jobs. It automatically infers schema from semi‑structured data (JSON, Parquet, CSV, etc.) and can handle nested structures, missing fields, and schema evolution without requiring explicit schema definitions. DynamicFrame also provides built‑in transformations such as resolveChoice, dropFields, and applyMapping that are optimized for Glue’s catalog integration. Because it stores data in a distributed key‑value format, it can efficiently perform joins and aggregations on large datasets. Additionally, DynamicFrame supports job bookmarks to avoid reprocessing data, and it can write directly to the Glue Data Catalog, making downstream services like Athena and Redshift aware of the schema. In contrast, a plain Spark DataFrame requires explicit schema handling, manual type casting, and does not automatically register schemas in the Glue catalog. Therefore, for typical Glue ETL pipelines that involve schema inference, nested data, and catalog integration, DynamicFrame offers a more convenient and robust solution.