Which of the following alternatives will lower costs without compromising average performance of the system or data integrity for the raw data?
Use reduced redundancy storage (RRS) for all data In S3. Use a combination of Spot Instances and Reserved Instances for Amazon EMR jobs. Use Reserved Instances for Amazon Redshift.
Use reduced redundancy storage (RRS) for PDF and .csv data in S3. Add Spot Instances to EMR jobs. Use Spot Instances for Amazon Redshift.
Use reduced redundancy storage (RRS) for PDF and .csv data In Amazon S3. Add Spot Instances to Amazon EMR jobs. Use Reserved Instances for Amazon Redshift.
Use reduced redundancy storage (RRS) for all data in Amazon S3. Add Spot Instances to Amazon EMR jobs. Use Reserved Instances for Amazon Redshift.
Explanations:
Using RRS for all data compromises data integrity, as RRS does not guarantee redundancy like standard S3 storage. This option may lower costs but is not a safe approach for critical data.
While RRS can be used for non-critical data, applying it to both PDF and CSV files may still risk data integrity. Additionally, using Spot Instances for Amazon Redshift is not recommended as it could lead to service interruptions.
This option optimizes costs by using RRS for non-critical PDF and CSV files, which is acceptable, and using Spot Instances for EMR jobs which are generally flexible. Reserved Instances for Amazon Redshift ensure consistent performance.
Similar to option A, using RRS for all data compromises data integrity. While the use of Spot Instances and Reserved Instances for EMR and Redshift is good, the risk associated with RRS for all data makes this option unsuitable.
I outline that the answer is:
Use reduced redundancy storage (RRS) for PDF and .csv data In Amazon S3. Add Spot Instances to Amazon EMR jobs. Use Reserved Instances for Amazon Redshift.
As far as I can tell, the answer is:
Use reduced redundancy storage (RRS) for PDF and .csv data In Amazon S3. Add Spot Instances to Amazon EMR jobs. Use Reserved Instances for Amazon Redshift.