An application places hundreds of 1 GB .csv files into an Amazon S3 bucket every hour. Each uploaded file must be converted to Apache Parquet format and written to an S3 bucket. Which solution provides the required functionality with the least operational overhead?
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Correct answer: Create an AWS Glue extract, transform, and load (ETL) job to convert the .csv files to Parquet and write the output files to an S3 bucket. Create an AWS Lambda function for each S3 PUT event to invoke the ETL job..
Why this is the answer
The correct solution uses AWS Glue ETL for data transformation and Lambda for event-driven invocation, offering the least operational overhead. AWS Glue is a fully managed ETL service designed for large-scale data processing, making it ideal for converting hundreds of 1 GB CSV files to Parquet without managing servers. Invoking the Glue job via a Lambda function triggered by S3 PUT events ensures real-time processing as files arrive. The other options are less efficient: A single Lambda function for direct conversion might hit memory or execution time limits for 1 GB files and is not designed for large-scale ETL. An Apache Spark job requires managing a Spark cluster, increasing operational overhead. Periodically querying with Athena and then converting is not real-time and adds unnecessary steps for a direct conversion task.
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