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deploying-on-aws

This Claude Code skill provides decision frameworks and implementation patterns for selecting and configuring Amazon Web Services across compute, storage, and database options. Use it when designing AWS architectures, choosing between services like Lambda versus ECS, selecting appropriate databases based on access patterns, implementing serverless or containerized solutions, and applying AWS Well-Architected Framework principles for cost-optimized and reliable infrastructure.

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SKILL.md

# AWS Patterns

## Purpose

This skill provides decision frameworks and implementation patterns for Amazon Web Services. Navigate AWS's 200+ services through proven selection criteria, architectural patterns, and Well-Architected Framework principles. Focus on practical service selection, cost-aware design, and modern 2025 patterns including Lambda SnapStart, EventBridge Pipes, and S3 Express One Zone.

Use this skill when designing AWS solutions, selecting services for specific workloads, implementing serverless or container architectures, or optimizing existing AWS infrastructure for cost, performance, and reliability.

## When to Use This Skill

Invoke this skill when:

- Choosing between Lambda, Fargate, ECS, EKS, or EC2 for compute workloads
- Selecting database services (RDS, Aurora, DynamoDB) based on access patterns
- Designing VPC architecture for multi-tier applications
- Implementing serverless patterns with API Gateway and Lambda
- Building container-based microservices on ECS or EKS
- Applying AWS Well-Architected Framework to designs
- Optimizing AWS costs while maintaining performance
- Implementing security best practices (IAM, KMS, encryption)

## Core Service Selection Frameworks

### Compute Service Selection

**Decision Flow:**

```
Execution Duration:
  <15 minutes → Evaluate Lambda
  >15 minutes → Evaluate containers or VMs

Event-Driven/Scheduled:
  YES → Lambda (serverless)
  NO → Consider traffic patterns

Containerized:
  YES → Need Kubernetes?
    YES → EKS
    NO → ECS (Fargate or EC2)
  NO → Evaluate EC2 or containerize first

Special Requirements:
  GPU/Windows/BYOL licensing → EC2
  Predictable high traffic → EC2 or ECS on EC2 (cost optimization)
  Variable traffic → Lambda or Fargate
```

**Quick Reference:**

| Workload | Primary Choice | Cost Model | Key Benefit |
|----------|---------------|------------|-------------|
| API Backend | Lambda + API Gateway | Pay per request | Auto-scale, no servers |
| Microservices | ECS on Fargate | Pay for runtime | Simple operations |
| Kubernetes Apps | EKS | $73/mo + compute | Portability, ecosystem |
| Batch Jobs | Lambda or Fargate Spot | Request/spot pricing | Cost efficiency |
| Long-Running | EC2 Reserved Instances | 30-60% savings | Predictable cost |

For detailed service comparisons including cost examples, performance characteristics, and use case guidance, see `references/compute-services.md`.

### Database Service Selection

**Decision Matrix by Access Pattern:**

| Access Pattern | Data Model | Primary Choice | Key Criteria |
|----------------|------------|----------------|--------------|
| Transactional (OLTP) | Relational | Aurora | Performance + HA |
| Simple CRUD | Relational | RDS PostgreSQL | Cost vs. features |
| Key-Value Lookups | NoSQL | DynamoDB | Serverless scale |
| Document Storage | JSON/BSON | DynamoDB | Flexibility vs. MongoDB compat |
| Caching | In-Memory | ElastiCache Redis | Speed + durability |
| Analytics (OLAP) | Columnar | Redshift/Athena | Dedicated vs. serverless |
| Time-Series | Timestamped | Timestream | Purpose-built |

**Query Complexity Guide:**

- **Simple Key-Value:** DynamoDB (single-digit ms latency)
- **Moderate Joins (2-3 tables):** Aurora or RDS (cost vs. performance)
- **Complex Analytics:** Redshift (dedicated) or Athena (serverless, query S3)
- **Real-Time Streams:** DynamoDB Streams + Lambda

For storage class selection, cost comparisons, and migration patterns, see `references/database-services.md`.

### Storage Service Selection

**Primary Decision Tree:**

```
Data Type:
  Objects (files, media) → S3 + lifecycle policies
  Blocks (databases, boot volumes) → EBS
  Shared Files (cross-instance) → Evaluate protocol

File Protocol Required:
  NFS (Linux) → EFS
  SMB (Windows) → FSx for Windows
  High-Performance HPC → FSx for Lustre
  Multi-Protocol + Enterprise → FSx for NetApp ONTAP
```

**Cost Comparison (1TB/month):**

| Service | Monthly Cost | Access Pattern |
|---------|--------------|----------------|
| S3 Standard | $23 | Frequent access |
| S3 Standard-IA | $12.50 | Infrequent (>30 days) |
| S3 Glacier Instant | $4 | Archive, instant retrieval |
| EBS gp3 | $80 | Block storage |
| EFS Standard | $300 | Shared files, frequent |
| EFS IA | $25 | Shared files, infrequent |

**Recommendation:** Use S3 for 80%+ of storage needs. Use EFS/FSx only when shared file access is required.

For S3 storage classes, EBS volume types, and lifecycle policy examples, see `references/storage-services.md`.

## Serverless Architecture Patterns

### Pattern 1: REST API (Lambda + API Gateway + DynamoDB)

**Architecture:**
```
Client → API Gateway (HTTP API) → Lambda → DynamoDB
                                        ↓
                                       S3 (file uploads)
```

**Use When:**
- Building RESTful APIs with CRUD operations
- Variable or unpredictable traffic
- Minimal operational overhead desired
- Pay-per-request cost model acceptable

**Cost Estimate (1M requests/month):**
- API Gateway: $3.50
- Lambda: $3.53
- DynamoDB: ~$7.50
- **Total: ~$15/month** (vs. Fargate ~$35+, EC2 ~$50+)

**Key Components:**
- API Gateway HTTP API (cheaper than REST API)
- Lambda with appropriate memory allocation (1024MB typically optimal)
- DynamoDB on-demand billing (for variable traffic)
- CloudWatch Logs for debugging

See `examples/cdk/serverless-api/` and `examples/terraform/serverless-api/` for complete implementations.

### Pattern 2: Event-Driven Processing (EventBridge + Lambda + SQS)

**Architecture:**
```
S3 Upload → EventBridge Rule → Lambda (process) → DynamoDB (metadata)
                                              ↓
                                            SQS (downstream tasks)
```

**Use When:**
- Asynchronous file processing
- Decoupled microservices communication
- Fan-out patterns (one event, multiple consumers)
- Need retry logic and dead-letter queues

**Key Features (2025):**
- **EventBridge Pipes:** Simplified source → filter → enrichment → target
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