An ETL job must process daily .csv files that users place into an S3 bucket. Each file is smaller than 100 MB. Which implementation is the most cost-effective?
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Correct answer: Write an AWS Glue Python shell job. Use pandas to transform the data..
Why this is the answer
The most cost-effective solution is an AWS Glue Python shell job using pandas. For small CSV files (under 100 MB), a Python shell job provides a serverless, pay-per-second execution environment that is significantly cheaper than Spark-based solutions. Pandas is highly efficient for data manipulation on single nodes, making it suitable for this scale. Using a custom Python application on EKS would incur significant operational overhead and cost for cluster management, even for small tasks. An Amazon EMR cluster or an AWS Glue PySpark job would be overkill and more expensive for processing small files, as Spark introduces overhead for distributed processing that isn't necessary here.
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