Operation: Each microservice is an independent unit that performs a specific function (such as user management, data analysis, appointment handling). These services are containerized using Docker, which means each operates in its own virtualized environment, ensuring independence and avoiding conflicts between services. Kubernetes orchestrates these containers, managing their deployment, scaling, and operation. It allows high availability and load balancing between containers to optimize performance.
Operations: Serves as the entry point for all external requests. Authenticates and authorizes requests, handles rate limiting, and provides additional layers of security. Data Flow: User requests pass through the API Gateway before being directed to the corresponding microservices.
Amazon RDS: Handles relational data, such as patient records or appointment information. Offers features like high availability, encryption, and ease of scaling. Amazon DynamoDB: Used for NoSQL data, such as real-time health records or wearable device data. Efficient for handling large volumes of data and quick queries.
Functionality: Executes code in response to events specific, such as changes in a database or a new API request. This is ideal for tasks that don't require a dedicated server permanently, such as ad-hoc data processing or automated tasks. Integration: Can be integrated with other AWS services to automate workflows and processes.
IAM and AWS Cognito: Manage identities and access, ensuring that only authorized users can access certain data and functions. AWS KMS and Secrets Manager: Protect encryption keys and other secrets, essential for maintaining the security of sensitive data.
ELB: Distributes incoming traffic among multiple service instances to avoid overload and downtime. Auto Scaling: Automatically adjusts the amount of resources based on demand, ensuring optimal performance without overprovisioning resources.
AWS CodePipeline and CodeBuild: Automate the process of development, from source code to building, testing, and deployment of applications. GitHub: Serves as a secure and managed version control repository.
CloudWatch: Monitors the health and performance of the application in real-time, collecting logs and metrics. ELK Stack or Amazon Elasticsearch Service: Provides tools for analyzing and visualizing large sets of log data, useful for issue detection and performance optimization.
Training and Model Deployment: SageMaker is used to create, train, and deploy machine learning models, such as disease prediction and resource customization. Integration: These models integrate with the rest of the architecture to provide real-time data-based predictive analytics and decision support.
Multi-AZ Deployment: Ensures that the application is distributed across multiple Availability Zones to prevent interruptions. Amazon S3: Used for regular backups, ensuring that data can be recovered in case of a failure or disaster.
Each of these sections works together to create an integrated and efficient system, capable of handling the complexities and demands of a modern health and wellness application.