Question

A data engineering team receives raw event data as JSON files in Amazon S3. Each record contains a nested array field called 'items', where each element holds product attributes for a single purchase. The ML team needs a flat, tabular dataset in which every product within an order becomes its own row so features can be computed per product. The team wants a low-code, visual approach that produces a reusable transformation recipe. Which approach best meets these requirements?

Accepted Answer

Use AWS Glue DataBrew and apply an 'unnest'/'explode' transformation on the 'items' array to flatten nested elements into individual rows within a saved recipe.

Answer

Configure an AWS Glue crawler to detect the schema, then rely on the crawler to automatically flatten the nested array during cataloging.

Answer

Use Amazon Athena to run a SELECT query on the JSON files and export the result set, treating the array column as a single string.

Answer

Load the JSON into SageMaker Feature Store, which will automatically normalize nested arrays into separate feature records.