Which solution will meet these requirements MOST cost-effectively?
Read the data from the tapes on premises. Stage the data in a local NFS storage. Use AWS DataSync to migrate the data to Amazon S3 Glacier Flexible Retrieval.
Use an on-premises backup application to read the data from the tapes and to write directly to Amazon S3 Glacier Deep Archive.
Order multiple AWS Snowball devices that have Tape Gateway. Copy the physical tapes to virtual tapes in Snowball. Ship the Snowball devices to AWS. Create a lifecycle policy to move the tapes to Amazon S3 Glacier Deep Archive.
Configure an on-premises Tape Gateway. Create virtual tapes in the AWS Cloud. Use backup software to copy the physical tape to the virtual tape.
Explanations:
This option involves reading data from tapes to local NFS storage, which requires significant time and effort. It also incurs additional costs for local storage and AWS DataSync, making it less cost-effective than other options.
While writing directly to Amazon S3 Glacier Deep Archive sounds efficient, it relies on the backup application’s capability to read from physical tapes and write to the cloud, which may be slower and could lead to complications due to limited internet bandwidth for 5 PB of data.
Using AWS Snowball with Tape Gateway allows for efficient migration of large volumes of data by creating virtual tapes and shipping the devices to AWS. This option reduces the need for high bandwidth during the transfer and is cost-effective, especially for compliance with long-term data storage.
Although configuring an on-premises Tape Gateway is feasible, it requires ongoing management and infrastructure costs, and the bandwidth limitation would hinder a quick migration of 5 PB of data, making it less cost-effective than using AWS Snowball.
I chart that the answer is:
Order multiple AWS Snowball devices that have Tape Gateway. Copy the physical tapes to virtual tapes in Snowball. Ship the Snowball devices to AWS. Create a lifecycle policy to move the tapes to Amazon S3 Glacier Deep Archive.