aws-solution-architect
Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.
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name aws-solution-architect description Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization. AWS Solution Architect Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates. Workflow Step 1: Gather Requirements Collect application specifications: - Application type (web app, mobile backend, data pipeline, SaaS) - Expected users and requests per second - Budget constraints (monthly spend limit) - Team size and AWS experience level - Compliance requirements (GDPR, HIPAA, SOC 2) - Availability requirements (SLA, RPO/RTO) Step 2: Design Architecture Run the architecture designer to get pattern recommendations: python scripts/architecture_designer.py --input requirements.json Example output: { "recommended_pattern" : "serverless_web" , "service_stack" : [ "S3" , "CloudFront" , "API Gateway" , "Lambda" , "DynamoDB" , "Cognito" ] , "estimated_monthly_cost_usd" : 35 , "pros" : [ "Low ops overhead" , "Pay-per-use" , "Auto-scaling" ] , "cons" : [ "Cold starts" , "15-min Lambda limit" , "Eventual consistency" ] } Select from recommended patterns: Serverless Web : S3 + CloudFront + API Gateway + Lambda + DynamoDB Event-Driven Microservices : EventBridge + Lambda + SQS + Step Functions Three-Tier : ALB + ECS Fargate + Aurora + ElastiCache GraphQL Backend : AppSync + Lambda + DynamoDB + Cognito See references/architecture_patterns.md for detailed pattern specifications. Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3. Step 3: Generate IaC Templates Create infrastructure-as-code for the selected pattern: # Serverless stack (CloudFormation) python scripts/serverless_stack.py --app-name my-app --region us-east-1 Example CloudFormation YAML output (core serverless resources): AWSTemplateFormatVersion: '2010-09-09' Transform: AWS::Serverless-2016-10-31 Parameters: AppName: Type: String Default: my-app Resources: ApiFunction: Type: AWS::Serverless::Function Properties: Handler: index.handler Runtime: nodejs20.x MemorySize: 512 Timeout: 30 Environment: Variables: TABLE_NAME: !Ref DataTable Policies: - DynamoDBCrudPolicy: TableName: !Ref DataTable Events: ApiEvent: Type: Api Properties: Path: /{proxy+} Method: ANY DataTable: Type: AWS::DynamoDB::Table Properties: BillingMode: PAY_PER_REQUEST AttributeDefinitions: - AttributeName: pk AttributeType: S - AttributeName: sk AttributeType: S KeySchema: - AttributeName: pk KeyType: HASH - AttributeName: sk KeyType: RANGE Full templates including API Gateway, Cognito, IAM roles, and CloudWatch logging are generated by serverless_stack.py and also available in references/architecture_patterns.md . Example CDK TypeScript snippet (three-tier pattern): import * as ecs from 'aws-cdk-lib/aws-ecs' ; import * as ec2 from 'aws-cdk-lib/aws-ec2' ; import * as rds from 'aws-cdk-lib/aws-rds' ; const vpc = new ec2. Vpc ( this , 'AppVpc' , { maxAzs : 2 }); const cluster = new ecs. Cluster ( this , 'AppCluster' , { vpc }); const db = new rds. ServerlessCluster ( this , 'AppDb' , { engine : rds. DatabaseClusterEngine . auroraPostgres ({ version : rds. AuroraPostgresEngineVersion . VER_15_2 , }), vpc, scaling : { minCapacity : 0.5 , maxCapacity : 4 }, }); Step 4: Review Costs Analyze estimated costs and optimization opportunities: python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000 Example output: { "current_monthly_usd" : 2000 , "recommendations" : [ { "action" : "Right-size RDS db.r5.2xlarge → db.r5.large" , "savings_usd" : 420 , "priority" : "high" } , { "action" : "Purchase 1-yr Compute Savings Plan at 40% utilization" , "savings_usd" : 310 , "priority" : "high" } , { "action" : "Move S3 objects >90 days to Glacier Instant Retrieval" , "savings_usd" : 85 , "priority" : "medium" } ] , "total_potential_savings_usd" : 815 } Output includes: Monthly cost breakdown by service Right-sizing recommendations Savings Plans opportunities Potential monthly savings Step 5: Deploy Deploy the generated infrastructure: # CloudFormation aws cloudformation create-stack \ --stack-name my-app-stack \ --template-body file://template.yaml \ --capabilities CAPABILITY_IAM # CDK cdk deploy # Terraform terraform init && terraform apply Step 6: Validate and Handle Failures Verify deployment and set up monitoring: # Check stack status aws cloudformation describe-stacks --stack-name my-app-stack # Set up CloudWatch alarms aws cloudwatch put-metric-alarm --alarm-name high-errors ... If stack creation fails: Check the failure reason: aws cloudformation describe-stack-events \ --stack-name my-app-stack \ --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]' Review CloudWatch Logs for Lambda or ECS errors. Fix the template or resource configuration. Delete the failed stack before retrying: aws cloudformation delete-stack --stack-name my-app-stack # Wait for deletion aws cloudformation wait stack-delete-complete --stack-name my-app-stack # Redeploy aws cloudformation create-stack ... Common failure causes: IAM permission errors → verify --capabilities CAPABILITY_IAM and role trust policies Resource limit exceeded → request quota increase via Service Quotas console Invalid template syntax → run aws cloudformation validate-template --template-body file://template.yaml before deploying Tools architecture_designer.py Generates architecture patterns based on requirements. python scripts/architecture_designer.py --input requirements.json --output design.json Input: JSON with app type, scale, budget, compliance needs Output: Recommended pattern, service stack, cost estimate, pros/cons serverless_stack.py Creates serverless CloudFormation templates. python scripts/serverless_stack.py --app-name my-app --region us-east-1 Output: Production-ready CloudFormation YAML with: API Gateway + Lambda DynamoDB table Cognito user pool IAM roles with least privilege CloudWatch logging cost_optimizer.py Analyzes costs and recommends optimizations. python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000 Output: Recommendations for: Idle resource removal Instance right-sizing Reserved capacity purchases Storage tier transitions NAT Gateway alternatives Quick Start MVP Architecture (< $100/month) Ask: "Design a serverless MVP backend for a mobile app with 1000 users" Result: - Lambda + API Gateway for API - DynamoDB pay-per-request for data - Cognito for authentication - S3 + CloudFront for static assets - Estimated: $20-50/month Scaling Architecture ($500-2000/month) Ask: "Design a scalable architecture for a SaaS platform with 50k users" Result: - ECS Fargate for containerized API - Aurora Serverless for relational data - ElastiCache for session caching - CloudFront for CDN - CodePipeline for CI/CD - Multi-AZ deployment Cost Optimization Ask: "Optimize my AWS setup to reduce costs by 30%. Current spend: $3000/month" Provide: Current resource inventory (EC2, RDS, S3, etc.) Result: - Idle resource identification - Right-sizing recommendations - Savings Plans analysis - Storage lifecycle policies - Target savings: $900/month IaC Generation Ask: "Generate CloudFormation for a three-tier web app with auto-scaling" Result: - VPC with public/private subnets - ALB with HTTPS - ECS Fargate with auto-scaling - Aurora with read replicas - Security groups and IAM roles Input Requirements Provide these details for architecture design: Requirement Description Example Application type What you're building SaaS platform, mobile backend Expected scale Users, requests/sec 10k users, 100 RPS Budget Monthly AWS limit $500/month max Team context Size, AWS experience 3 devs, intermediate Compliance Regulatory needs HIPAA, GDPR, SOC 2 Availability Uptime requirements 99.9% SLA, 1hr RPO JSON Format: { "application_type" : "saas_platform" , "expected_users" : 10000 , "requests_per_second" : 100 , "budget_monthly_usd" : 500 , "team_size" : 3 , "aws_experience" : "intermediate" , "compliance" : [ "SOC2" ] , "availability_sla" : "99.9%" } Output Formats Architecture Design Pattern recommendation with rationale Service stack diagram (ASCII) Monthly cost estimate and trade-offs IaC Templates CloudFormation YAML : Production-ready SAM/CFN templates CDK TypeScript : Type-safe infrastructure code Terraform HCL : Multi-cloud compatible configs Cost Analysis Current spend breakdown with optimization recommendations Priority action list (high/medium/low) and implementation checklist Reference Documentation Document Contents references/architecture_patterns.md 6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region references/service_selection.md Decision matrices for compute, database, storage, messaging references/best_practices.md Serverless design, cost optimization, security hardening, scalability
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