Building a Serverless ETL Pipeline from RDS PostgreSQL to S3 with AWS Glue

Quick Answer

This 'choose three' question is describing a full ETL pipeline, and the services line up with the three stages any such pipeline needs: a source, a transformation engine, and a destination. AWS Glue is the transformation engine here because it's a serverless ETL service capable of reading from a JDBC source like RDS for PostgreSQL, performing the filtering, joining with data already in S3, and aggregating logic the scenario describes, all without the team having to provision or manage the underlying compute themselves. Amazon S3 serves as the destination, providing durable, cost-effective object storage for the transformed daily output, and it's also part of the join operation itself, since some of the data Glue joins against already lives in S3. Because the requirement explicitly calls for minimal operational overhead, the combination leans on managed, serverless services rather than anything the team would need to provision, patch, or scale by hand, which is a big part of why Glue fits so well for the transformation step instead of a self-managed compute cluster. The pattern to recognize is that ETL-style questions like this one are usually testing whether you can identify the natural source, transform, and destination roles in a pipeline and match each to the AWS service purpose-built for that role, rather than trying to force one service to do more than it's designed for. When a scenario describes filtering, joining, and aggregating data from a relational database before loading it elsewhere, expect Glue to be the transformation layer regardless of which specific source or destination services round out the rest of the pipeline.