You store transactional data in Amazon DynamoDB and get single-digit millisecond performance. However, when you want to run analytics, machine learning (ML), or reporting on that same data, you face a gap: your flexible, semi-structured DynamoDB schemas don’t align with the flat, columnar formats that analytics engines require. Bridging this gap typically means building and maintaining custom ETL pipelines, which adds development cost and operational overhead.
This is a companion discussion topic for the original entry at https://aws.amazon.com/blogs/big-data/improve-dynamodb-analytics-with-aws-glue-zero-etl-schema-and-partition-controls/