AWS Certified Data Engineer Associate DEA-C01 Practice Question
An analytics team needs to run nightly Python-based ETL jobs that transform raw clickstream data stored in Amazon S3. The solution must allow developers to version-control and reuse their scripts, support third-party Python libraries, and minimize cluster management tasks. Which AWS service best meets these requirements?
Create a PL/pgSQL stored procedure in Amazon Redshift and invoke it nightly with AWS Lambda
Launch an Amazon EMR cluster and execute the Python scripts with bootstrap actions
Use an Amazon Athena scheduled query that calls the Python scripts
Run the scripts as AWS Glue jobs on the serverless Spark platform
AWS Glue lets you author and run Python (or Scala) ETL scripts in a fully managed, serverless Apache Spark environment. Developers commit scripts to a repository and Glue automatically provisions and scales the underlying resources, while also allowing wheel or egg files to be packaged with the job for additional Python libraries. Amazon EMR can run PySpark scripts, but operators must create, size, patch, and terminate clusters, adding operational overhead. Amazon Redshift stored procedures use PL/pgSQL rather than Python and are intended for in-database transformations, not large-scale file processing on S3. Amazon Athena scheduled queries accept only SQL, so they cannot execute custom Python code. Therefore, AWS Glue is the most appropriate choice.
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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