Question
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? (Choose two.)
- A. Configure AWS Glue triggers to run the ETL jobs every hour. Correct
- 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. Correct
- E. Use the Redshift Data API to load transformed data into Amazon Redshift.
Explanation
AWS Glue triggers (Option A) automate the scheduling of ETL jobs, ensuring that the data pipeline runs every hour with minimal operational overhead. AWS Glue connections (Option D) are used to define and manage the necessary database connection information for accessing Amazon RDS, MongoDB, and Amazon Redshift, reducing the complexity of managing these connections manually.
