Explanation:
AWS Glue triggers provide a native and automated way to schedule ETL jobs (such as running them every hour) with minimal operational overhead. Additionally, AWS Glue connections are natively designed to securely store connection information and establish connectivity between AWS Glue and data stores like Amazon RDS, MongoDB, and Amazon Redshift. Using Lambda for scheduling adds unnecessary operational overhead.
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Question 7 A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour. Which combination of tasks will meet these requirements with the LEAST operational overhead? (Select TWO.)
A
Configure AWS Glue triggers to run the ETL jobs every hour.
B
Use AWS Glue DataBrew to clean and prepare the data for analytics.
C
Use AWS Lambda functions to schedule and run the ETL jobs every hour.
D
Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
E
Use the Redshift Data API to load transformed data into Amazon Redshift.
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