How can these requirements be met by using the LEAST amount of ongoing management overhead and causing MINIMAL disruption to the existing system?
Set up an AWS Storage Gateway, file gateway appliance on-premises. Use the MAM solution to extract the videos from the current archive and push them into the file gateway. Use the catalog of faces to build a collection in Amazon Rekognition. Build an AWS Lambda function that invokes the Rekognition Javascript SDK to have Rekognition pull the video from the Amazon S3 files backing the file gateway, retrieve the required metadata, and push the metadata into the MAM solution.
Set up an AWS Storage Gateway, tape gateway appliance on-premises. Use the MAM solution to extract the videos from the current archive and push them into the tape gateway. Use the catalog of faces to build a collection in Amazon Rekognition. Build an AWS Lambda function that invokes the Rekognition Javascript SDK to have Amazon Rekognition process the video in the tape gateway, retrieve the required metadata, and push the metadata into the MAM solution.
Configure a video ingestion stream by using Amazon Kinesis Video Streams. Use the catalog of faces to build a collection in Amazon Rekognition. Stream the videos from the MAM solution into Kinesis Video Streams. Configure Amazon Rekognition to process the streamed videos. Then, use a stream consumer to retrieve the required metadata, and push the metadata into the MAM solution. Configure the stream to store the videos in Amazon S3.
Set up an Amazon EC2 instance that runs the OpenCV libraries. Copy the videos, images, and face catalog from the on-premises library into an Amazon EBS volume mounted on this EC2 instance. Process the videos to retrieve the required metadata, and push the metadata into the MAM solution, while also copying the video files to an Amazon S3 bucket.
Explanations:
Using AWS Storage Gateway (File Gateway) allows the company to migrate the video content with minimal disruption. The use of Rekognition for automated metadata extraction based on faces is appropriate, and Lambda can orchestrate the processing. The solution requires minimal ongoing management.
Tape Gateway is designed for virtual tape library (VTL) use cases, which would not be suitable for the video content that needs to be processed by Rekognition. This option adds unnecessary complexity and is not optimized for video storage and metadata enrichment.
Kinesis Video Streams is ideal for live video streaming, but the company is dealing with archived videos stored on tape, not real-time video ingestion. This option involves unnecessary complexity and overhead, such as real-time streaming setup, when file-based processing would suffice.
Setting up an EC2 instance with OpenCV for manual metadata processing would involve high management overhead, as it requires handling video processing, face matching, and metadata extraction manually. This is a more complex solution compared to the others, leading to higher operational overhead.