Which solution will meet these requirements MOST cost-effectively?
Store the logs in Amazon S3. Use Amazon Athena for analysis.
Store the logs in Amazon RDS. Use a database client for analysis.
Store the logs in Amazon OpenSearch Service (Amazon Elasticsearch Service). Use Amazon OpenSearch Service (Amazon Elasticsearch Service) for analysis.
Store the logs in an Amazon EMR cluster. Use a supported open-source framework for SQL-based analysis.
Explanations:
Storing logs in Amazon S3 and using Amazon Athena allows for on-demand, serverless querying of large datasets with SQL, making it a scalable and cost-effective solution. Athena charges only for the data scanned during queries, which is ideal for occasional analysis.
Storing logs in Amazon RDS would be costly and inefficient for handling large volumes of logs. RDS is designed for transactional databases, not for large-scale log storage and analysis. Additionally, RDS has limitations in scalability and performance for the required workload.
Amazon OpenSearch Service (formerly Elasticsearch) is designed for real-time search and analytics but is not optimal for running standard SQL queries on large log datasets. It’s more suitable for full-text search and analytics, not for on-demand weekly SQL-based analysis of large volumes of data.
Storing logs in Amazon EMR and using open-source frameworks like Apache Hive or Presto for SQL-based analysis can be complex and costly for occasional analysis. EMR incurs costs even when idle, making it less cost-effective compared to serverless solutions like Athena.
I conclude that the answer is:
Store the logs in Amazon S3. Use Amazon Athena for analysis.