AWS Certified Data Engineer Associate DEA-C01 Practice Question
Every day at 02:00 UTC, a healthcare company must ingest the previous day's CSV file from an Amazon S3 bucket into a staging table in Amazon Redshift. The team wants a fully managed, serverless solution that minimizes cost and ongoing administration while reliably running at the scheduled time. Which approach best meets these requirements?
Deploy an Amazon Managed Workflows for Apache Airflow (MWAA) environment and schedule a DAG that issues a Redshift COPY command for the file in S3.
Launch a transient Amazon EMR cluster each night that runs a Spark job to copy the file from S3 to Redshift, then terminates the cluster.
Configure an Amazon Kinesis Data Firehose delivery stream with a Lambda transformation to send data to Redshift and enable the stream at 02:00 UTC using the AWS CLI.
Create a cron-based Amazon EventBridge rule that starts an AWS Glue ETL job, which reads the CSV file from S3 and writes it to Amazon Redshift.
An Amazon EventBridge rule can invoke an AWS Glue job on a cron schedule without provisioning or managing servers. Glue provides a native Redshift connection that issues a COPY operation under the hood, so the job can efficiently load the CSV file from S3 into the staging table. EventBridge and Glue are both serverless and incur charges only when rules fire or jobs run, meeting the cost-effectiveness and low-ops goals.
Launching an EMR cluster requires cluster provisioning, node management, and longer billing intervals. MWAA introduces additional infrastructure to manage and a higher baseline cost for the environment. Kinesis Data Firehose is optimized for continuous streaming, cannot be "scheduled" to start at a specific time, and would run (and bill) continuously, increasing cost and complexity.
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Why is AWS Glue preferred over Amazon EMR for this use case?
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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