AWS architecture example with mermaid

An AI generated AWS architecture example with mermaid js support

AWS architecture

architecture-beta
    group user(logos:aws-api-gateway)[User]
    group real_time(logos:aws-ec2)[Real Time Layer]
    group analytics(logos:aws-ec2)[Analytics Layer]
    group batch(logos:aws-batch)[Batch Processing Layer]
    group report(logos:aws-quicksight)[Reporting Layer]

    service api_gateway(logos:aws-api-gateway)[API Gateway] in user

    service lambda(logos:aws-lambda)[Lambda] in real_time
    service dynamodb(logos:aws-dynamodb)[Dynamodb] in real_time
    service sns(logos:aws-sns)[SNS] in real_time
    service kinesis(logos:aws-kinesis)[Kinesis] in real_time

    service s3(logos:aws-s3)[S3] in analytics
    service lambda_inventory(logos:aws-lambda)[Lambda Inventory] in analytics
    service athena(logos:aws-athena)[AWS Athena] in analytics

    service lambda_batch(logos:aws-lambda)[Lambda Batch] in batch
    service dynamodb_batch(logos:aws-dynamodb)[Dynamodb] in batch

    service quicksight(logos:aws-quicksight)[Dashboard] in report

    api_gateway:R --> L:lambda

    lambda:R --> L:dynamodb
    lambda:T --> L:sns
    lambda:T --> L:kinesis

    kinesis:R --> L:lambda_inventory
    lambda_inventory:R --> L:s3 
    s3:R --> L:athena
    kinesis:T --> L:lambda_batch
    lambda_batch:R --> L:dynamodb_batch
    athena:R --> L:quicksight

Source Code

architecture-beta
    group user(logos:aws-api-gateway)[User]
    group real_time(logos:aws-ec2)[Real Time Layer]
    group analytics(logos:aws-ec2)[Analytics Layer]
    group batch(logos:aws-batch)[Batch Processing Layer]
    group report(logos:aws-quicksight)[Reporting Layer]

    service api_gateway(logos:aws-api-gateway)[API Gateway] in user

    service lambda(logos:aws-lambda)[Lambda] in real_time
    service dynamodb(logos:aws-dynamodb)[Dynamodb] in real_time
    service sns(logos:aws-sns)[SNS] in real_time
    service kinesis(logos:aws-kinesis)[Kinesis] in real_time

    service s3(logos:aws-s3)[S3] in analytics
    service lambda_inventory(logos:aws-lambda)[Lambda Inventory] in analytics
    service athena(logos:aws-athena)[AWS Athena] in analytics

    service lambda_batch(logos:aws-lambda)[Lambda Batch] in batch
    service dynamodb_batch(logos:aws-dynamodb)[Dynamodb] in batch

    service quicksight(logos:aws-quicksight)[Dashboard] in report

    api_gateway:R --> L:lambda

    lambda:R --> L:dynamodb
    lambda:T --> L:sns
    lambda:T --> L:kinesis

    kinesis:R --> L:lambda_inventory
    lambda_inventory:R --> L:s3 
    s3:R --> L:athena
    kinesis:T --> L:lambda_batch
    lambda_batch:R --> L:dynamodb_batch
    athena:R --> L:quicksight

Explanation:

• Users: Represents customers interacting with the system via an API.
• API Gateway: Acts as the entry point for user requests, routing them to the Lambda function.
• Lambda Function (Order Placement): This function handles the order placement process, saving order details in DynamoDB and notifying customers via SNS. It also pushes order events to a Kinesis stream for real-time processing.
• DynamoDB (Order Table): A NoSQL database storing order details for quick access.
• SNS (Order Confirmation): A messaging service used to send order confirmations to customers.
• Kinesis Stream (Order Events): Real-time stream capturing order events for immediate processing and analysis.
• Lambda Function (Inventory Update): This function is triggered by the Kinesis stream. It updates the inventory table in DynamoDB based on the order events.
• DynamoDB (Inventory Table): A NoSQL database holding inventory information.
• Lambda Function (Data Aggregation): This function reads from the Kinesis stream and aggregates order data (e.g., sales totals, popular items) for analysis.
• S3 (Data Lake): Object storage where the aggregated order data is stored for long-term analysis.
• Amazon Athena: A serverless query engine used to analyze the aggregated order data in S3.
• Dashboards: Visualizations and reports generated from the analyzed data.

Key Advantages of this Architecture:

• Real-time Processing: Order events are processed in real-time by the Kinesis stream and Lambda functions, enabling immediate updates and analytics.
• Asynchronous Processing: Inventory updates are processed asynchronously, decoupling order placement from inventory management for a more responsive user experience.
• Scalability: Lambda functions and Kinesis scale automatically based on the volume of order events, ensuring the system remains responsive under high load.
• Cost-effectiveness: Pay-as-you-go pricing for Lambda functions and Kinesis, allowing you to pay only for the resources you use.
• Data Analysis: The Kinesis stream and Athena provide the foundation for real-time and historical data analysis, generating valuable insights into sales trends and customer behavior.

Example Use Case:

This architecture is suitable for e-commerce platforms, online marketplaces, and other applications that require real-time order processing, inventory management, and data analysis. The architecture allows for a highly scalable and cost-effective solution that can handle a large volume of orders and provide valuable insights into customer behavior and business performance.