To meet the requirements with the least operational overhead, the data engineer should leverage the native, managed features of AWS Glue. AWS Glue connections are Data Catalog objects that securely store and manage the connection information for data sources like Amazon RDS and MongoDB, and targets like Amazon Redshift. This centralizes connectivity management and simplifies the ETL script.

For the hourly execution requirement, AWS Glue triggers provide a built-in, serverless scheduling mechanism. A time-based trigger can be configured to run the ETL job every hour without the need to provision or manage external scheduling infrastructure like AWS Lambda functions, thus minimizing operational overhead.

1. AWS Glue Developer Guide

"Defining connections in the AWS Glue Data Catalog": This guide explains that an AWS Glue connection is a Data Catalog object used to store connection information for data stores. It explicitly lists Amazon RDS

MongoDB

and Amazon Redshift as supported through JDBC and MongoDB connection types

respectively. This supports the use of AWS Glue connections (Option D) as the standard

managed approach.

2. AWS Glue Developer Guide

"Triggering jobs in AWS Glue": This section details the different types of triggers

including scheduled triggers. It states

"You can define a time-based schedule for your jobs... This scheduling capability runs on a cron-like schedule." This confirms that triggers (Option A) are the native

low-overhead mechanism for hourly job execution.

3. AWS Glue Developer Guide

"Moving data to and from Amazon Redshift": This documentation describes the recommended method for loading data into Amazon Redshift from an AWS Glue ETL job

which involves using a JDBC connection (managed by AWS Glue Connections) and a temporary Amazon S3 directory. This reinforces that Option D is the correct approach for connectivity

making Option E a less standard

higher-overhead alternative.