AWS Glue provides a fully managed serverless ETL service with native Spark support. By migrating all ETL jobs to Glue, the company eliminates the need to manage Amazon EMR clusters. Glue workflows can orchestrate and automate multiple ETL jobs, ensuring dependencies are respected and executions are automated with minimal manual intervention. This satisfies the requirement for a serverless, Spark-based, fully managed solution.

Kinesis Data Firehose has built-in support for Splunk as a destination, making the integration straightforward. Using a CloudWatch Logs subscription filter directly to Firehose simplifies the data flow, eliminating the need for additional Lambda functions or custom integrations.

By calculating a checksum (e.g., SHA-256) before upload and providing it in the upload request, Amazon S3 verifies the checksum on receipt and rejects the upload if the data is corrupted. This provides automatic, independent validation of data integrity without requiring full data downloads.

AWS Glue Data Quality rules can be integrated directly into the AWS Glue ETL pipeline to automatically check for missing or invalid data in the daily file. You can use Data Quality Definition Language (DQDL) to define rules that verify the completeness and validity of the data before the pipeline writes the output. This solution ensures that the previous day's file is only overwritten if the new data is complete and valid, minimizing manual intervention and operational effort.
While Lambda could be used, it requires additional development and integration outside of the existing Glue ETL pipeline. This adds complexity and operational overhead.
The "Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field" approach assumes that filling missing values with the most common ones is acceptable, but it might not always be the correct solution for all cases. The requirement specifies that the file should be validated and replaced only if it's complete and valid.
Using Athena to drop missing rows doesn't fully address the problem, as it could result in loss of data and still requires manual intervention to validate the file before copying it. Additionally, it introduces unnecessary steps compared to using built-in AWS Glue Data Quality capabilities.