AWS Certified Data Engineer Associate DEA-C01 Practice Question
A retail company receives a 10-GB CSV file in an Amazon S3 bucket every night. The file must be loaded into Amazon Redshift as soon as it arrives. The solution must be fully managed, cost-effective, and must avoid re-loading the same file if the job is restarted after a failure. Which approach meets these requirements?
Configure AWS DataSync to move the file into an Amazon Redshift Spectrum external table and run an INSERT statement into the target table.
Schedule an Amazon EMR cluster to start nightly, run a Spark script that uses the COPY command to load the file into Amazon Redshift, and terminate the cluster afterward.
Create an Amazon EventBridge rule for the s3:ObjectCreated event to start an AWS Glue job that copies the file into Amazon Redshift, and enable AWS Glue job bookmarks.
Use Amazon Kinesis Data Analytics with an S3 source and a Redshift destination to stream the file contents into Amazon Redshift.
An Amazon EventBridge rule that listens for the s3:ObjectCreated event can start an AWS Glue job whenever the nightly file lands in the bucket, providing a serverless, pay-as-you-go ingestion workflow. Enabling AWS Glue job bookmarks causes the job to record which S3 objects have already been processed, so a retry does not reload the same file. The combination removes the need to manage EC2 instances and is less expensive and simpler than operating Amazon EMR, AWS Data Pipeline, or streaming services for a once-per-day batch workload.
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What are AWS Glue job bookmarks, and why are they used?
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Why is AWS Glue more cost-effective and simpler than 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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