Question

A data engineer receives deeply nested JSON documents from a SaaS API, landed in Amazon S3. Each document contains arrays of order line items nested inside customer objects. The team needs to load this data into an Amazon Redshift data warehouse where analysts run SQL joins across flattened relational tables. The engineer is building an AWS Glue ETL job using DynamicFrames. Which approach most efficiently converts the nested structure into normalized, relational tables suitable for Redshift?

Accepted Answer

Use the Relationalize transform on the DynamicFrame to flatten nested structures and unnest arrays into separate tables linked by generated keys

Answer

Apply the ResolveChoice transform to cast the nested arrays into string columns and load them as-is into Redshift

Answer

Use the ApplyMapping transform to rename each nested field individually into a single wide flat table

Answer

Convert the DynamicFrame to a Spark DataFrame and use the SelectFields transform to drop the nested arrays before loading