aws-solution-architect（AWS solution 架构师）

AWS Solution Architect

Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.

Table of Contents

• Trigger Terms
• Workflow
• Tools
• Quick Start
• Input Requirements
• Output Formats

Trigger Terms

Use this skill when you encounter:

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

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.

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

# Output: CloudFormation YAML template ready to deploy

Step 4: Review Costs

Analyze estimated costs and optimization opportunities:

python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000

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

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 ...

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:

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)
• Configuration specifications
• Monthly cost estimate
• Scaling characteristics
• Trade-offs and limitations

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
• Optimization recommendations with savings
• Priority action list (high/medium/low)
• Implementation checklist

Reference Documentation

Limitations

• Lambda: 15-minute execution, 10GB memory max
• API Gateway: 29-second timeout, 10MB payload
• DynamoDB: 400KB item size, eventually consistent by default
• Regional availability varies by service
• Some services have AWS-specific lock-in

Troubleshooting

Success Criteria

• Cost accuracy: Monthly AWS bill stays within 10% of the architecture estimate produced by cost_optimizer.py.
• Availability: Production workloads meet or exceed the target SLA (99.9% uptime for three-tier, 99.95% for multi-region).
• Recovery time: RTO under 4 hours and RPO under 1 hour for all production architectures with disaster recovery configured.
• Deployment speed: Infrastructure provisioned from generated IaC templates in under 30 minutes for serverless stacks and under 60 minutes for three-tier stacks.
• Security posture: Zero critical findings in AWS Security Hub within 30 days of deployment; all resources encrypted at rest and in transit.
• Scaling response: Auto-scaling responds to traffic spikes within 2 minutes, handling 10x baseline load without manual intervention.
• Operational overhead: Team spends less than 4 hours per week on infrastructure operations after initial deployment.

Scope & Limitations

This skill covers:

• AWS architecture design for startups and growth-stage companies (serverless, three-tier, microservices, data pipelines, IoT, multi-region patterns)
• Infrastructure-as-code generation for CloudFormation (SAM), CDK (TypeScript), and Terraform (HCL)
• Cost analysis, right-sizing recommendations, and Savings Plans evaluation
• Service selection guidance for compute, database, storage, networking, and security

This skill does NOT cover:

• Multi-cloud or hybrid-cloud architectures (Azure, GCP) -- see engineering/cloud-migration-specialist/ for cross-cloud strategies
• Application-level code, business logic, or framework-specific implementation -- see engineering/senior-fullstack/ for fullstack development
• Compliance audit execution or regulatory evidence collection -- see ra-qm-team/ for SOC 2, HIPAA, GDPR, and ISO compliance skills
• AWS account management, organization policies, or billing disputes -- see AWS Support or engineering/ms365-tenant-manager/ for tenant administration patterns

Integration Points

Tool Reference

architecture_designer.py

Purpose: Generates architecture pattern recommendations based on application requirements. Analyzes app type, expected scale, budget, team experience, and compliance needs to recommend the optimal AWS architecture pattern with full service configurations and cost estimates.

Usage:

from scripts.architecture_designer import ArchitectureDesigner

designer = ArchitectureDesigner(requirements)
pattern = designer.recommend_architecture_pattern()
checklist = designer.generate_service_checklist()

Constructor Parameters:

Requirements Dictionary Fields:

Methods:

Example:

from scripts.architecture_designer import ArchitectureDesigner

requirements = {
    "application_type": "saas_platform",
    "expected_users": 10000,
    "requests_per_second": 100,
    "budget_monthly_usd": 500,
    "team_size": 3,
    "aws_experience": "intermediate",
    "compliance": ["SOC2"],
    "data_size_gb": 50
}

designer = ArchitectureDesigner(requirements)
result = designer.recommend_architecture_pattern()
print(result['pattern_name'])       # "Serverless Web Application"
print(result['estimated_cost'])     # {"monthly_usd": ..., "breakdown": {...}}
print(result['services'])           # Full service stack with configurations

Output Format: Returns a dictionary with keys: pattern_name, description, use_case, services (nested service configurations), estimated_cost (with monthly_usd and breakdown), pros, cons, and scaling_characteristics.

Supported Patterns:

• Serverless Web Application (< 10k users)
• Modern Three-Tier Application (10k-100k users)
• Multi-Region High Availability (100k+ users)
• Serverless Mobile Backend (mobile app type)
• Event-Driven Microservices (microservices type)
• Real-Time Data Pipeline (data pipeline type)
• IoT Platform (IoT type)

serverless_stack.py

Purpose: Generates production-ready infrastructure-as-code templates for serverless applications. Produces CloudFormation (SAM), CDK (TypeScript), and Terraform (HCL) configurations with API Gateway, Lambda, DynamoDB, Cognito, IAM roles, and CloudWatch logging preconfigured.

Usage:

from scripts.serverless_stack import ServerlessStackGenerator

generator = ServerlessStackGenerator(app_name, requirements)
cfn_template = generator.generate_cloudformation_template()
cdk_stack = generator.generate_cdk_stack()
terraform_config = generator.generate_terraform_configuration()

Constructor Parameters:

Requirements Dictionary Fields:

Methods:

Example:

from scripts.serverless_stack import ServerlessStackGenerator

generator = ServerlessStackGenerator("my-saas-app", {"region": "us-west-2"})

# Generate CloudFormation template
cfn = generator.generate_cloudformation_template()
with open("template.yaml", "w") as f:
    f.write(cfn)

# Generate CDK stack
cdk = generator.generate_cdk_stack()
with open("lib/stack.ts", "w") as f:
    f.write(cdk)

# Generate Terraform config
tf = generator.generate_terraform_configuration()
with open("main.tf", "w") as f:
    f.write(tf)

Output Format: Each method returns a string containing the full IaC template. Templates include: DynamoDB table (single-table design with PK/SK), Lambda function (Node.js 18.x, 512 MB, 10s timeout), API Gateway (REST, Cognito auth, CORS, throttling), Cognito User Pool (email sign-in, optional MFA), IAM roles (least privilege), and CloudWatch log group (7-day retention). All templates output: API URL, User Pool ID, User Pool Client ID, and Table Name.

cost_optimizer.py

Purpose: Analyzes current AWS resource inventory and spending to generate prioritized cost optimization recommendations. Evaluates compute (EC2, Lambda), storage (S3), databases (RDS, DynamoDB), networking (NAT Gateway, VPC endpoints), and general optimizations (CloudWatch Logs, Elastic IPs, budget alerts).

Usage:

from scripts.cost_optimizer import CostOptimizer

optimizer = CostOptimizer(current_resources, monthly_spend)
analysis = optimizer.analyze_and_optimize()
checklist = optimizer.generate_optimization_checklist()

Constructor Parameters:

Resources Dictionary Fields:

Methods:

Example:

from scripts.cost_optimizer import CostOptimizer

resources = {
    "ec2_instances": [
        {"cpu_utilization": 5, "pricing": "on-demand"},
        {"cpu_utilization": 65, "pricing": "on-demand"}
    ],
    "s3_buckets": [
        {"name": "app-assets", "size_gb": 200, "storage_class": "STANDARD", "has_lifecycle_policy": False}
    ],
    "nat_gateways": [{"id": "nat-1"}, {"id": "nat-2"}],
    "multi_az_required": False,
    "has_budget_alerts": False,
    "has_cost_explorer": False
}

optimizer = CostOptimizer(resources, monthly_spend=3000)
result = optimizer.analyze_and_optimize()

print(f"Current spend: ${result['current_monthly_spend']}")
print(f"Potential savings: ${result['potential_monthly_savings']}")
print(f"Savings: {result['savings_percentage']}%")
for rec in result['priority_actions']:
    print(f"  [{rec['priority']}] {rec['service']}: {rec['recommendation']}")

Output Format: analyze_and_optimize() returns a dictionary with keys: current_monthly_spend (float), potential_monthly_savings (float), optimized_monthly_spend (float), savings_percentage (float), recommendations (list of dicts with service, type, issue, recommendation, potential_savings, priority), and priority_actions (top 5 high-priority recommendations sorted by savings).