Which solution meets the company’s needs and takes the LEAST amount of time?
Use a gateway endpoint for Amazon S3. Migrate the data to Amazon S3. Import the data into Aurora.
Upgrade the Direct Connect link to 500 Mbps. Copy the data to Amazon S3. Import the data into Aurora.
Order an AWS Snowmobile and copy the database backup to it. Have AWS import the data into Amazon S3. Import the backup into Aurora.
Order four 50-TB AWS Snowball devices and copy the database backup onto them. Have AWS import the data into Amazon S3. Import the data into Aurora.
Explanations:
While using S3 as an intermediary is a common approach, migrating 143 TB over a 100 Mbps Direct Connect connection will take a very long time. This is because the data still needs to traverse the network connection. Gateway endpoints optimize S3 access from within the VPC but do not increase the bandwidth of the Direct Connect connection itself.
Upgrading the Direct Connect link to 500 Mbps will improve transfer speeds compared to 100 Mbps. However, transferring 143 TB even at 500 Mbps will still take a significant amount of time. This option only addresses the network bottleneck but does not consider other faster data transfer methods.
AWS Snowmobile is designed for exabyte-scale data transfers. Using it for 143 TB is overkill and would be significantly slower and more expensive than using Snowball devices. The overhead of coordinating a Snowmobile transfer is substantial.
AWS Snowball devices are the most appropriate solution for this data volume and the given network bandwidth. Using multiple Snowball devices allows for parallel data transfer, significantly reducing the migration time. While there is a logistical component of shipping the devices, it is still faster than transferring 143TB over a 100 Mbps or even a 500 Mbps Direct Connect connection. The data is copied to the Snowballs, shipped to AWS, imported into S3, and then restored to Aurora. This minimizes network transfer time and hence the overall migration time.