AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company receives 10 GB of comma-separated value (CSV) files in an Amazon S3 bucket each night. A data engineer must convert the files to compressed, partitioned Apache Parquet format in the same bucket to lower Amazon Athena query costs while minimizing infrastructure management. Which solution meets these requirements with the LEAST operational overhead?
Launch an Amazon EMR cluster running Apache Hive, and use a daily Step Functions workflow to convert the CSV files to Parquet format in S3.
Configure an Amazon S3 Event Notification to invoke an AWS Lambda function that reads each CSV file and writes an equivalent Parquet file back to S3.
Create an AWS Glue crawler to catalog the incoming CSV data and schedule an AWS Glue Spark ETL job to write the data as compressed, partitioned Parquet files to the same S3 bucket.
Deploy an Apache Spark application on Amazon EC2 instances started by a cron job that reads the CSV files and outputs Parquet files to S3.
AWS Glue is a serverless, managed Spark service that can crawl the inbound CSV files, catalog the schema, and run an ETL job that writes compressed, partitioned Parquet files back to Amazon S3. Glue automatically provisions and scales the required resources, so no clusters or servers need to be managed. An EMR cluster or EC2-hosted Spark job would meet the technical need but imposes instance lifecycle and patching overhead. A Lambda function cannot reliably process 10 GB files within the service's memory and 15-minute execution limits, especially when performing Spark-style columnar conversions.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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