Which solution requires the LEAST effort to be able to query this data?
Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
Use AWS Glue to catalogue the data and Amazon Athena to run queries.
Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
Explanations:
AWS Data Pipeline is suitable for data movement and ETL processes but requires more setup to transform data and load it into Amazon RDS. This adds complexity and effort compared to directly querying data in S3.
AWS Glue can catalog both structured and unstructured data in S3, allowing Amazon Athena to query the data directly using standard SQL without needing to move or transform it significantly. This is the most efficient solution requiring the least effort.
AWS Batch is designed for running batch jobs but would require significant effort to set up ETL processes to prepare data for queries in Amazon Aurora, which is more complex and requires data to be moved from S3.
AWS Lambda is event-driven and would require additional effort to set up data transformation before using Amazon Kinesis Data Analytics, which is not designed for querying static data in S3. This adds unnecessary complexity.
I categorize that the answer is:
Use AWS Glue to catalogue the data and Amazon Athena to run queries.