A company runs a daily ETL pipeline implemented as Python scripts on a large EC2 instance to clean, transform, enrich, and compress terabytes of user interaction data stored in S3. The process takes over 20 hours, and they want to move off EC2 to a managed, serverless solution with minimal development effort. Which approach meets these requirements?

The correct answer is to create an AWS Glue job, convert the scripts to PySpark, run the Glue job to process the data, and store results in S3. AWS Glue is a fully managed, serverless ETL service designed for large-scale data processing. Converting Python scripts to PySpark allows leveraging Glue's distributed processing capabilities, significantly reducing the 20-hour processing time and eliminating the need to manage EC2 instances. Loading data into Amazon Redshift and using SQL is less flexible for complex transformations and might require significant re-engineering of existing Python logic. Loading into DynamoDB and using Lambda is unsuitable for terabytes of data due to Lambda's execution limits and the unsuitability of DynamoDB for large-scale analytical processing. Implementing each script as a separate Lambda function with Step Functions is also not ideal for terabytes of data due to Lambda's resource limits and the overhead of orchestrating many small functions for a single large ETL process.