Which analytics strategy is MOST cost-effective for the company to use?

Explanations:

While Amazon Elasticsearch Service (ES) is suitable for search and analytics, it can become costly for large datasets due to the need for maintaining a cluster, especially with the volume of data (200 GB per month). Additionally, operational overhead may increase with ES, making it less cost-effective for the company’s needs.

Using AWS Glue Data Catalog to create a table and querying the data with Amazon Athena is highly cost-effective. Athena charges based on the amount of data scanned per query, and since the company has structured data in S3, this allows for easy querying without the need for a dedicated cluster. Visualizing results in Amazon QuickSight can also be done efficiently and affordably.

Amazon EMR is suitable for big data processing but can be costly due to the operational overhead of maintaining clusters, especially for just monthly analytics. It requires more management and can incur significant costs compared to serverless options like Athena. Using QuickSight for visualization adds additional costs, making this option less cost-effective.

Amazon Redshift is a powerful data warehouse solution but typically involves higher fixed costs due to cluster management, regardless of the actual usage. For a use case that involves only monthly analytics on 200 GB of data, this option would not be cost-effective compared to serverless alternatives like Athena. QuickSight for visualization also adds to the overall costs.