A data engineer is building an hourly AWS Glue ETL pipeline that reads from Amazon RDS and MongoDB, transforms data, and loads it into Amazon Redshift. Which combination of actions will achieve this with the LEAST operational overhead? (Choose two.)

Configuring AWS Glue triggers to run ETL jobs every hour is the most suitable option for scheduling because Glue triggers are designed for this purpose, integrating natively with Glue jobs for minimal operational overhead. Using AWS Glue connections to establish connectivity is correct because Glue connections centralize and manage access credentials for various data sources and targets, including Amazon RDS, MongoDB, and Amazon Redshift, simplifying secure access within the Glue environment. Using AWS Lambda functions to schedule jobs would add unnecessary complexity and operational overhead compared to native Glue triggers. AWS Glue DataBrew is for data cleaning and preparation, not for scheduling or connectivity, and while potentially useful in the overall pipeline, it doesn't address the core requirements of scheduling and connectivity. The Redshift Data API is for interacting with Redshift, but it's not the primary mechanism for loading large-scale transformed data from Glue; Glue's Redshift connector is more efficient for this.