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URL: https://docs.aws.amazon.com/solutions/media2cloud-on-aws/

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Overview

This Guidance helps you facilitate the migration of your digital assets to the cloud, allowing you to take full advantage of the latest advancements in asset management and prepare content for distribution to media consumers. It sets up serverless ingestion and analysis workflows to move your video assets and associated metadata to the AWS Cloud. During the migration, this Guidance analyzes and extracts machine learning metadata from your video. It also comes with a web interface so you can immediately start ingesting and analyzing your content.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

👁 Architecture diagram
Step 1
An Amazon Cognito user pool to provide a user directory.
Step 2
An Amazon API Gateway RESTful API endpoint, which is configured to use AWS Identity and Access Management (IAM) authentication.
Step 3
An Amazon CloudFront distribution that hosts the web application artifacts, such as minimized JavaScript files and graphics stored in the web bucket.
Step 4
An AWS Step Functions main state machine which serves as the entry point to the backend ingestion and analysis workflows.
Step 5
An Step Functions ingestion sub-state machine that orchestrates the ingestion process by media file type and generates proxies for ingested media. It uses AWS Elemental MediaConvert for video and audio files and open-source tools for image files and documents.
Step 6
A Step Functions analysis sub-state machine that is responsible for the analysis process. It consists of Step Functions that run analysis jobs with Amazon Rekognition, Amazon Transcribe, Amazon Comprehend, and Amazon Textract.
Step 7
Amazon DynamoDB tables to store artifacts generated during the ingestion and analysis processes, such as overall status, pointers to where intermediate files are stored, and state machine run tokens.
Step 8
An Amazon OpenSearch Service cluster, which stores ingestion attributes and machine learning metadata, and facilitates your search and discovery needs.
Step 9
Four Amazon Simple Storage Service (Amazon S3) buckets store: uploaded content, file proxies that the Guidance generates during ingestion, static web application artifacts, and access logs for services used.
Step 10
Amazon CloudWatch event rules that are logged when specific tasks undergo state changes.
Step 11
Amazon EventBridge used by an internal queue management system where the backlog system notifies workflows (state machines) when a queued artificial intelligence and machine learning (AI/ML) request has been processed.
Step 12
An AWS IoT Core topic that allows the ingestion and analysis workflows to communicate with the front-end web application asynchronously through publish or subscribe MQTT messaging.
Step 13
Amazon Simple Notification Service (Amazon SNS) topics to allow Amazon Rekognition to publish job status in the video analysis workflow, and to support custom integration with your system.

Deploy with confidence

Everything you need to launch this Guidance in your account is right here.

Let's make it happen

A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment. The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

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