Manish Kumar

Posted on Dec 11

From Concept to Cloud: The Ultimate AWS Architecture for High-Traffic Platforms

#architecture #aws #productivity #devops

1. Solution Overview

The proposed solution is a cloud-native, microservices-based, event-driven architecture designed to handle millions of concurrent users with sub-second response times. The platform leverages AWS managed services to achieve 99.99% availability, horizontal scalability, and global reach while maintaining strong consistency for booking transactions.

Key Business Objectives:

Handle 10M+ daily active users with <200ms API response times
Process 1M+ events per second for real-time personalization
Ensure zero double-bookings through strong consistency guarantees
Support multi-region deployment for global low-latency access
Achieve <1 hour RTO and <5 minutes RPO for disaster recovery

Architectural Patterns: Microservices architecture with event-driven communication, CQRS (Command Query Responsibility Segregation) for read/write separation, Lambda architecture for real-time and batch processing, and API Gateway pattern for unified access.

2. Architecture Components

AWS Services & Resources

Compute Layer

Amazon EKS (v1.28): Managed Kubernetes for core microservices
- Node Groups: m6i.2xlarge (8 vCPU, 32 GB RAM) for stateless services
- Spot instances for non-critical workloads (70% cost reduction)
- Auto-scaling: 10-100 nodes based on CPU >70% and custom metrics
AWS Lambda: Serverless functions for event processing
- Memory: 1024-3096 MB based on function complexity
- Timeout: 30-900 seconds for async operations
- Provisioned concurrency for latency-sensitive functions
AWS Fargate: Container orchestration for batch jobs and admin services
- Task definitions: 2-4 vCPU, 8-16 GB memory

Database Layer

Amazon Aurora PostgreSQL Global Database (v15.4): Primary transactional database
- Instance type: db.r6g.4xlarge (16 vCPU, 128 GB RAM)
- Multi-AZ: 1 primary + 2 read replicas per region
- Cross-region replicas in 2 additional regions (us-east-1, eu-west-1, ap-southeast-1)
- Storage: Auto-scaling from 10GB to 128TB
Amazon DynamoDB Global Tables: User sessions, preferences, and real-time signals
- On-demand capacity mode for unpredictable traffic
- Point-in-time recovery enabled
- DAX cluster (dax.r5.large) for <1ms read latency
Amazon ElastiCache for Redis (v7.0): Multi-tier caching
- Cluster mode: cache.r6g.xlarge (4 vCPU, 26.32 GB RAM)
- 3 nodes per shard, 3 shards for horizontal scaling
- Global Datastore for multi-region caching
Amazon OpenSearch (v2.11): Search engine for property listings
- Instance type: r6g.2xlarge.search (8 vCPU, 64 GB RAM)
- 3 master nodes, 6 data nodes across 3 AZs
- 500GB EBS gp3 storage per node (16,000 IOPS)

Storage Layer

Amazon S3: Object storage for media assets
- Standard tier: Property images, documents
- Intelligent-Tiering: User uploads with lifecycle policies
- Glacier Flexible Retrieval: Archival data >90 days
- Versioning enabled with MFA delete protection
Amazon EFS: Shared file system for containerized applications
- Performance mode: General Purpose
- Throughput mode: Elastic (auto-scales)
- 100GB provisioned capacity

Networking Layer

Amazon VPC: Multi-tier network architecture
- CIDR: 10.0.0.0/16 (65,536 IPs)
- Public subnets: 10.0.1.0/24, 10.0.2.0/24, 10.0.3.0/24 (per AZ)
- Private app subnets: 10.0.11.0/24, 10.0.12.0/24, 10.0.13.0/24
- Private data subnets: 10.0.21.0/24, 10.0.22.0/24, 10.0.23.0/24
- NAT Gateways: 3 (one per AZ) in public subnets
Application Load Balancer (ALB): Layer 7 load balancing
- Internet-facing ALB for external traffic
- Internal ALB for microservices communication
- Sticky sessions with cookie-based routing
- Connection draining: 300 seconds
Amazon CloudFront: Global CDN with 450+ edge locations
- Origin: S3 (static assets) and ALB (dynamic content)
- Cache TTL: 86400s (static), 0s (dynamic with smart caching)
- Origin shield enabled for reduced origin load
- Field-level encryption for sensitive data
Amazon Route 53: DNS with health checks and failover
- Latency-based routing for global users
- Failover routing to secondary region
- Health checks every 30 seconds

Security Services

AWS IAM: Role-based access control
- Service accounts for each microservice with least privilege
- OIDC provider integration for EKS pod identities
- MFA enforcement for console access
AWS Secrets Manager: Secrets and credentials management
- Automatic rotation every 30 days
- Encryption with customer-managed KMS keys
AWS KMS: Encryption key management
- Customer-managed keys for Aurora, DynamoDB, S3
- Automatic key rotation annually
- CloudHSM integration for high-security requirements
AWS WAF: Web application firewall
- Managed rule groups: Core rule set, SQL injection, XSS
- Rate limiting: 2000 requests per 5 minutes per IP
- Geo-blocking for sanctioned countries
AWS Shield Advanced: DDoS protection
- 24/7 DDoS response team access
- Cost protection for scaling during attacks
Amazon GuardDuty: Threat detection
- Continuous monitoring for malicious activity
- Integration with EventBridge for automated response
AWS Security Hub: Centralized security posture
- CIS AWS Foundations Benchmark compliance
- Automated remediation with Lambda

Monitoring & Logging

Amazon CloudWatch: Metrics, logs, and alarms
- Metrics: Custom application metrics with 1-minute resolution
- Logs: Centralized logging with 90-day retention
- Alarms: 50+ alarms for critical metrics (CPU, memory, latency, errors)
- Dashboards: Real-time operational dashboards
AWS X-Ray: Distributed tracing
- Sampling rate: 10% for normal traffic, 100% for errors
- Service map visualization for dependency analysis
AWS CloudTrail: API audit logging
- Multi-region trail enabled
- Log file integrity validation
- S3 lifecycle to Glacier after 90 days

CI/CD Services

AWS CodePipeline: Orchestration of deployment pipeline
- Source: GitHub with webhook triggers
- Build stage: CodeBuild for Docker image creation
- Deploy stage: EKS with blue-green deployment
AWS CodeBuild: Container image building
- Build spec: Docker multi-stage builds
- Cache: S3-backed for faster builds
- Compute: BUILD_GENERAL1_LARGE (8 GB memory, 4 vCPUs)
AWS CodeDeploy: Deployment automation
- Deployment configuration: Blue-green with 10% traffic shifting every 5 minutes
- Automatic rollback on CloudWatch alarm breach

Additional Managed Services

Amazon EventBridge: Event bus for microservices communication
- Custom event buses per domain (bookings, properties, users)
- Event archive with 30-day retention
Amazon SQS: Asynchronous task queues
- Standard queues for non-critical processing
- FIFO queues for ordered operations (booking confirmation)
- Dead-letter queues with 14-day retention
Amazon SNS: Pub/sub notifications
- Topics for email, SMS, and mobile push notifications
- Message filtering for targeted delivery
Amazon SES: Transactional email delivery
- Dedicated IP pool for reputation management
- Open and click tracking enabled
Amazon Cognito: User authentication and authorization
- User pools: 10M+ users with MFA support
- Identity pools for temporary AWS credentials
- Social login: Google, Facebook, Apple
AWS Step Functions: Workflow orchestration
- Booking workflow: Search → Reserve → Payment → Confirm
- Express workflows for high-throughput operations

Infrastructure-as-Code Tools

Terraform (v1.6+): Primary IaC tool for AWS resource provisioning

Why Terraform: Multi-cloud compatibility, rich ecosystem, state management with S3 backend and DynamoDB locking, extensive AWS provider support, reusable modules for consistency
Module Structure:
- terraform/modules/networking: VPC, subnets, security groups
- terraform/modules/compute: EKS, Lambda, Fargate
- terraform/modules/database: Aurora, DynamoDB, ElastiCache
- terraform/modules/storage: S3, EFS
- terraform/modules/security: IAM roles, KMS, Secrets Manager
Remote State: S3 bucket booking-platform-tfstate with versioning and encryption

Helm (v3.13+): Kubernetes package manager for application deployment

Charts for each microservice with configurable values
Shared charts for common patterns (monitoring, ingress)

AWS CDK (TypeScript v2.110+): For complex Step Functions workflows and Lambda functions

Type safety for infrastructure code
High-level constructs for patterns

Third-Party Tools/Platforms

Container Orchestration

Kubernetes v1.28: Container orchestration platform
Helm Charts: Custom charts for microservices
Kustomize: Environment-specific overlays (dev, staging, prod)
ArgoCD (v2.9+): GitOps continuous delivery
- Automated sync from Git repositories
- Self-healing capabilities
- Multi-cluster management

CI/CD Platforms

GitHub Actions: CI pipeline for testing and building
- Workflow: Lint → Test → Security scan → Build → Push to ECR
- Self-hosted runners on EC2 for faster builds
ArgoCD: CD for Kubernetes deployments

Monitoring & Observability

Prometheus (v2.48+): Metrics collection and storage
- Scrape interval: 30 seconds
- Retention: 15 days
- Node exporter, kube-state-metrics for cluster insights
Grafana (v10.2+): Visualization and dashboards
- 20+ pre-built dashboards for infrastructure and application metrics
- Alerting integration with PagerDuty and Slack
Datadog: APM and log management (alternative/supplementary)
- Distributed tracing across microservices
- Real user monitoring (RUM) for frontend performance

Security & Compliance

Trivy: Container image vulnerability scanning
- Integrated in CI pipeline with severity threshold: HIGH
Falco: Runtime security monitoring in Kubernetes
- Detects anomalous behavior in containers
OPA/Gatekeeper: Policy enforcement in Kubernetes
- Admission controller for policy validation
- Policies for resource limits, image registries, network policies

Message Streaming

Apache Kafka on Amazon MSK (v3.6): Event streaming platform
- Cluster: kafka.m5.2xlarge (8 vCPU, 32 GB RAM) × 6 brokers
- Partition: 100 partitions per topic
- Retention: 7 days
- Topics: user-events, booking-events, property-updates, payment-events

Programming Languages & Frameworks

Application Layer

Node.js (v20 LTS): User service, search service, recommendation service
- Framework: NestJS for enterprise-grade architecture
- ORM: Prisma for database access with type safety
Java (OpenJDK 17): Booking service, payment service
- Framework: Spring Boot 3.2 with Spring Cloud for microservices patterns
- Reactive programming with Project Reactor for high concurrency
Python (v3.11): ML/recommendation engine, data processing pipelines
- Framework: FastAPI for high-performance APIs
- Libraries: Pandas, NumPy, scikit-learn, TensorFlow
Go (v1.21): API Gateway, notification service (high-performance services)
- Framework: Gin for HTTP routing
- gRPC for inter-service communication

Frontend

React (v18) with Next.js (v14) for server-side rendering
TypeScript for type safety
Redux Toolkit for state management

Scripting & Automation

Python: AWS Lambda functions, automation scripts
Bash: Infrastructure maintenance scripts
TypeScript: AWS CDK infrastructure code

Data Processing

Apache Flink (v1.18): Stream processing
- Deployed on EKS with 20 task managers
- Checkpointing every 5 minutes to S3

Hardware/Compute Specifications

EKS Node Groups

General Purpose (Microservices)

Instance type: m6i.2xlarge
- vCPU: 8, Memory: 32 GB, Network: Up to 12.5 Gbps
- Rationale: Balanced compute/memory for stateless services
Auto-scaling: 10-100 nodes
- Scale-up: CPU >70% for 3 minutes
- Scale-down: CPU <30% for 10 minutes
Pod limits: 58 pods per node

Memory-Optimized (Caching/Data Services)

Instance type: r6i.2xlarge
- vCPU: 8, Memory: 64 GB
- Rationale: High memory for caching layers and data processing
Auto-scaling: 3-20 nodes

Compute-Optimized (CPU-Intensive Tasks)

Instance type: c6i.4xlarge
- vCPU: 16, Memory: 32 GB
- Rationale: ML inference, search indexing
Auto-scaling: 2-15 nodes

Lambda Configurations

API Functions: 1024 MB, 30s timeout, 1000 concurrent executions
Event Processors: 2048 MB, 300s timeout, 5000 concurrent executions
Scheduled Jobs: 3008 MB, 900s timeout, 10 concurrent executions

RDS/Aurora Instances

Production: db.r6g.4xlarge
- vCPU: 16, Memory: 128 GB, Network: Up to 10 Gbps
- Connection pool: 500 max connections per instance
Read Replicas: db.r6g.2xlarge (2 per region)

ElastiCache Clusters

Instance: cache.r6g.xlarge
- vCPU: 4, Memory: 26.32 GB
- Cluster: 3 shards × 3 nodes = 9 nodes total
- Max connections: 65,000 per node

OpenSearch Nodes

Master nodes: r6g.large.search (3 nodes)
Data nodes: r6g.2xlarge.search (6 nodes)
- vCPU: 8, Memory: 64 GB, Storage: 500GB gp3 EBS

3. Architecture Diagram

┌─────────────────────────────────────────────────────────────────────────────┐
│                           REGION: us-east-1 (Primary)                        │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                         Global Services Layer                         │   │
│  │  ┌────────────┐  ┌─────────────┐  ┌──────────────┐  ┌─────────────┐│   │
│  │  │ Route 53   │  │ CloudFront  │  │    WAF       │  │   Shield    ││   │
│  │  │(Latency    │  │(CDN: 450+   │  │(Rate Limit:  │  │  Advanced   ││   │
│  │  │ Routing)   │  │ Edge Locs)  │  │ 2K req/5min) │  │  (DDoS)     ││   │
│  │  └─────┬──────┘  └──────┬──────┘  └──────┬───────┘  └─────────────┘│   │
│  └────────┼─────────────────┼─────────────────┼──────────────────────────┘   │
│           │                 │                 │                              │
│  ┌────────▼─────────────────▼─────────────────▼──────────────────────────┐   │
│  │                    VPC: 10.0.0.0/16 (3 AZs)                           │   │
│  │                                                                        │   │
│  │  ┌──────────────────────────────────────────────────────────────┐    │   │
│  │  │              PUBLIC SUBNETS (10.0.1-3.0/24)                   │    │   │
│  │  │  ┌──────────────────┐  ┌──────────────────┐  ┌────────────┐ │    │   │
│  │  │  │  Internet-facing │  │   NAT Gateway    │  │   Bastion  │ │    │   │
│  │  │  │       ALB        │  │  (3 per AZ)      │  │    Host    │ │    │   │
│  │  │  │ (HTTPS:443)      │  │                  │  │ (Mgmt Only)│ │    │   │
│  │  │  └────────┬─────────┘  └────────┬─────────┘  └────────────┘ │    │   │
│  │  └───────────┼──────────────────────┼──────────────────────────┘    │   │
│  │              │                      │                                │   │
│  │  ┌───────────▼──────────────────────▼────────────────────────────┐  │   │
│  │  │         PRIVATE APP SUBNETS (10.0.11-13.0/24)                 │  │   │
│  │  │  ┌──────────────────────────────────────────────────────────┐ │  │   │
│  │  │  │        Amazon EKS Cluster (k8s v1.28)                     │ │  │   │
│  │  │  │  ┌─────────────┐  ┌──────────────┐  ┌─────────────────┐ │ │  │   │
│  │  │  │  │   User      │  │   Property   │  │    Booking      │ │ │  │   │
│  │  │  │  │   Service   │  │   Service    │  │    Service      │ │ │  │   │
│  │  │  │  │  (Node.js)  │  │  (Node.js)   │  │    (Java)       │ │ │  │   │
│  │  │  │  │  3-10 pods  │  │  5-20 pods   │  │   5-30 pods     │ │ │  │   │
│  │  │  │  └──────┬──────┘  └──────┬───────┘  └────────┬────────┘ │ │  │   │
│  │  │  │  ┌──────▼──────┐  ┌──────▼───────┐  ┌────────▼────────┐ │ │  │   │
│  │  │  │  │   Search    │  │   Payment    │  │  Notification   │ │ │  │   │
│  │  │  │  │   Service   │  │   Service    │  │    Service      │ │ │  │   │
│  │  │  │  │  (Node.js)  │  │   (Java)     │  │     (Go)        │ │ │  │   │
│  │  │  │  │  5-15 pods  │  │  3-15 pods   │  │   2-10 pods     │ │ │  │   │
│  │  │  │  └──────┬──────┘  └──────┬───────┘  └────────┬────────┘ │ │  │   │
│  │  │  │         │                │                    │           │ │  │   │
│  │  │  │  ┌──────▼────────────────▼────────────────────▼────────┐ │ │  │   │
│  │  │  │  │         Internal Application Load Balancer          │ │ │  │   │
│  │  │  │  └──────────────────────────────────────────────────────┘ │ │  │   │
│  │  │  └──────────────────────────────────────────────────────────┘ │  │   │
│  │  │                                                                 │  │   │
│  │  │  ┌──────────────────────────────────────────────────────────┐ │  │   │
│  │  │  │           Lambda Functions (Serverless Layer)            │ │  │   │
│  │  │  │  • Event Processors (User Signals Processing)            │ │  │   │
│  │  │  │  • Image Processing (Thumbnails, Optimization)           │ │  │   │
│  │  │  │  • Scheduled Jobs (Reports, Cleanup)                     │ │  │   │
│  │  │  │  • Stream Processing (Kafka → DynamoDB)                  │ │  │   │
│  │  │  └──────────────────────────────────────────────────────────┘ │  │   │
│  │  │                                                                 │  │   │
│  │  │  ┌──────────────────────────────────────────────────────────┐ │  │   │
│  │  │  │        Event-Driven Architecture Components              │ │  │   │
│  │  │  │  ┌────────────────┐  ┌──────────────┐  ┌──────────────┐ │ │  │   │
│  │  │  │  │  EventBridge   │  │     SQS      │  │     SNS      │ │ │  │   │
│  │  │  │  │ (Event Bus)    │  │  (Queues)    │  │  (Pub/Sub)   │ │ │  │   │
│  │  │  │  └────────────────┘  └──────────────┘  └──────────────┘ │ │  │   │
│  │  │  │  ┌────────────────────────────────────────────────────┐ │ │  │   │
│  │  │  │  │   Amazon MSK (Kafka v3.6 - 6 Brokers)              │ │ │  │   │
│  │  │  │  │   Topics: user-events, booking-events, payments    │ │ │  │   │
│  │  │  │  └────────────────────────────────────────────────────┘ │ │  │   │
│  │  │  └──────────────────────────────────────────────────────────┘ │  │   │
│  │  └─────────────────────────────────────────────────────────────┘  │   │
│  │                                                                     │   │
│  │  ┌──────────────────────────────────────────────────────────────┐ │   │
│  │  │         PRIVATE DATA SUBNETS (10.0.21-23.0/24)               │ │   │
│  │  │                                                               │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │     Aurora PostgreSQL Global Database (v15.4)          │  │ │   │
│  │  │  │  Primary: db.r6g.4xlarge (16 vCPU, 128GB)             │  │ │   │
│  │  │  │  Read Replicas: 2x db.r6g.2xlarge per region          │  │ │   │
│  │  │  │  Cross-region replication: <1s latency                 │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  │                                                               │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │        DynamoDB Global Tables (On-Demand)              │  │ │   │
│  │  │  │  • user-sessions (TTL: 24h)                            │  │ │   │
│  │  │  │  • user-preferences                                    │  │ │   │
│  │  │  │  • user-signals (real-time events)                     │  │ │   │
│  │  │  │  • booking-state-machine                               │  │ │   │
│  │  │  │  + DAX Cluster (dax.r5.large - <1ms reads)            │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  │                                                               │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │    ElastiCache Redis Global Datastore (v7.0)          │  │ │   │
│  │  │  │  3 shards × 3 nodes (cache.r6g.xlarge)                │  │ │   │
│  │  │  │  Use cases: Session cache, API cache, Rate limiting   │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  │                                                               │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │       Amazon OpenSearch Service (v2.11)                │  │ │   │
│  │  │  │  Master: 3x r6g.large.search (HA)                     │  │ │   │
│  │  │  │  Data: 6x r6g.2xlarge.search (500GB gp3 each)         │  │ │   │
│  │  │  │  Indices: properties, users, bookings                  │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  └───────────────────────────────────────────────────────────────┘ │   │
│  │                                                                     │   │
│  │  ┌──────────────────────────────────────────────────────────────┐ │   │
│  │  │                   Storage & CDN Layer                         │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │              Amazon S3 (Multi-Region)                  │  │ │   │
│  │  │  │  • booking-platform-media (Images, Videos)             │  │ │   │
│  │  │  │  • booking-platform-documents (Contracts, IDs)         │  │ │   │
│  │  │  │  • booking-platform-backups (DB dumps, Snapshots)      │  │ │   │
│  │  │  │  • booking-platform-logs (CloudWatch, Access logs)     │  │ │   │
│  │  │  │  Versioning: Enabled | MFA Delete: Enabled             │  │ │   │
│  │  │  │  Lifecycle: Standard → Intelligent-Tiering → Glacier   │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  │                                                               │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │        Amazon EFS (Shared File System)                 │  │ │   │
│  │  │  │  Mount targets in each AZ for EKS pods                │  │ │   │
│  │  │  │  Performance: General Purpose | Throughput: Elastic    │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  └───────────────────────────────────────────────────────────────┘ │   │
│  │                                                                     │   │
│  │  ┌──────────────────────────────────────────────────────────────┐ │   │
│  │  │              Security & Identity Services                     │ │   │
│  │  │  ┌──────────────┐  ┌────────────┐  ┌────────────────────┐   │ │   │
│  │  │  │   Cognito    │  │    IAM     │  │  Secrets Manager   │   │ │   │
│  │  │  │ (User Pools) │  │  (Roles)   │  │  (DB Creds, API)   │   │ │   │
│  │  │  └──────────────┘  └────────────┘  └────────────────────┘   │ │   │
│  │  │  ┌──────────────┐  ┌────────────┐  ┌────────────────────┐   │ │   │
│  │  │  │     KMS      │  │ GuardDuty  │  │  Security Hub      │   │ │   │
│  │  │  │(CMK for all) │  │(Threat Det)│  │(CIS Compliance)    │   │ │   │
│  │  │  └──────────────┘  └────────────┘  └────────────────────┘   │ │   │
│  │  └───────────────────────────────────────────────────────────────┘ │   │
│  │                                                                     │   │
│  │  ┌──────────────────────────────────────────────────────────────┐ │   │
│  │  │            Monitoring & Observability Stack                   │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │  CloudWatch (Metrics, Logs, Alarms, Dashboards)        │  │ │   │
│  │  │  │  • 50+ alarms (CPU, Memory, Latency, Error Rate)       │  │ │   │
│  │  │  │  • Log retention: 90 days                              │  │ │   │
│  │  │  │  • Custom metrics: 1-min resolution                    │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │  Prometheus + Grafana (on EKS)                         │  │ │   │
│  │  │  │  • 20+ dashboards (Infrastructure + Application)       │  │ │   │
│  │  │  │  • Alerting: PagerDuty, Slack integration              │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  │  ┌────────────────────────────────────────────────────────┐  │ │   │
│  │  │  │  AWS X-Ray (Distributed Tracing)                       │  │ │   │
│  │  │  │  • Service map visualization                           │  │ │   │
│  │  │  │  • Sampling: 10% normal, 100% errors                   │  │ │   │
│  │  │  └────────────────────────────────────────────────────────┘  │ │   │
│  │  └───────────────────────────────────────────────────────────────┘ │   │
│  └─────────────────────────────────────────────────────────────────┘   │
│                                                                          │
│  ┌──────────────────────────────────────────────────────────────────┐  │
│  │                        CI/CD Pipeline                             │  │
│  │  GitHub → GitHub Actions → CodeBuild → ECR → ArgoCD → EKS       │  │
│  │  (Source)   (Test/Scan)    (Build)    (Registry) (Deploy)       │  │
│  └──────────────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│              SECONDARY REGIONS: eu-west-1, ap-southeast-1                    │
│  • Aurora read replicas (cross-region replication <1s)                      │
│  • DynamoDB Global Tables (bidirectional replication)                       │
│  • ElastiCache Global Datastore (sub-second replication)                    │
│  • S3 Cross-Region Replication (CRR) for critical data                      │
│  • CloudFront edge caching for regional users                               │
│  • Route 53 latency-based routing to nearest region                         │
└─────────────────────────────────────────────────────────────────────────────┘

Security Boundaries:
━━━━━━━━━━━━━━━━━━━
• Public Subnets: Internet Gateway, ALB, NAT Gateway
• Private App Subnets: EKS, Lambda (outbound via NAT)
• Private Data Subnets: RDS, ElastiCache, OpenSearch (no internet)
• Security Groups: Least privilege port access
• NACLs: Subnet-level protection
• WAF: Layer 7 filtering at CloudFront/ALB

Data Flow:

User requests hit Route 53 → CloudFront (cached static content) → WAF filtering → ALB
ALB routes to appropriate microservice in EKS based on path
Microservices read from ElastiCache (cache hit) or query Aurora/DynamoDB (cache miss)
Search queries go to OpenSearch for property listings
Booking transactions write to Aurora with strong consistency, emit events to EventBridge/Kafka
Event processors (Lambda/Flink) consume events, update DynamoDB user signals
Asynchronous tasks (notifications, analytics) processed via SQS/SNS
Static assets served from S3 via CloudFront with edge caching

4. High Availability & Disaster Recovery

Multi-AZ Deployment Strategy

Application Layer: EKS nodes distributed across 3 AZs (us-east-1a, us-east-1b, us-east-1c) with pod anti-affinity rules ensuring service replicas run in different AZs
Database Layer: Aurora Multi-AZ with 1 primary + 2 read replicas, automatic failover in <30 seconds
Cache Layer: ElastiCache cluster mode with 3 shards, each with nodes in 3 AZs for 99.99% availability
Load Balancers: ALB cross-zone load balancing enabled, health checks every 30 seconds with 2 consecutive failures triggering deregistration

Auto-Scaling Policies

EKS Cluster Auto-scaling:

Horizontal Pod Autoscaler (HPA): Target CPU 70%, memory 75%, custom metrics (request rate >1000/sec per pod)
Cluster Autoscaler: Adds nodes when pods are unschedulable due to resource constraints
Karpenter (alternative): Provisions nodes in <1 minute based on pod requirements

Target Tracking Policies:

Booking Service: Scale when p99 latency >500ms
Search Service: Scale when request queue depth >100
Payment Service: Scale when active connections >80% of max

Backup and Restore Procedures

Aurora Automated Backups:

Continuous backup to S3 with point-in-time recovery (PITR) to any second within retention period
Retention: 35 days
Backup window: 02:00-04:00 UTC (low-traffic period)
Cross-region backup copy to us-west-2 for geographic redundancy

DynamoDB Backups:

Point-in-time recovery enabled (continuous backups for 35 days)
On-demand backups weekly, retained for 90 days
Cross-region replication via Global Tables provides automatic DR

S3 Versioning & Lifecycle:

Object versioning enabled for all buckets
Cross-Region Replication (CRR) to us-west-2 for critical data
MFA delete protection on production buckets

EKS etcd Backups:

Velero for Kubernetes backup to S3
Daily full backups, retained for 30 days
Includes persistent volumes, secrets, configmaps

RTO/RPO Targets

Component	RPO	RTO	Strategy
Aurora Database	<5 minutes	<1 hour	Multi-AZ + PITR + Cross-region replica promotion
DynamoDB	<1 minute	<15 minutes	Global Tables with continuous replication
ElastiCache	<1 minute	<30 minutes	Multi-AZ cluster with automatic failover
EKS Workloads	0 (stateless)	<15 minutes	Multi-AZ pods + ArgoCD auto-sync redeploy
S3 Data	0	<5 minutes	Cross-region replication + 99.999999999% durability
Overall System	<5 minutes	<1 hour	Regional failover with Route 53 health checks

Failover Mechanisms

Database Failover:

Aurora: Automatic failover to standby replica in 30-120 seconds, DNS endpoint remains same
Global Database: Manual promotion of secondary region in <1 minute for DR scenario
Connection pooling with retry logic handles transient failures

Application Failover:

Route 53 health checks monitor ALB endpoint every 30 seconds
Failure threshold: 3 consecutive failures (90 seconds detection)
Automatic DNS failover to secondary region (eu-west-1) with 60-second TTL
Multi-region active-passive with warm standby (10% capacity in secondary)

Automated Healing:

EKS: Failed pods automatically restarted by kubelet, rescheduled by kube-scheduler
ALB: Unhealthy targets removed from rotation, health checks every 30 seconds
Lambda: Automatic retry with exponential backoff for failed invocations

5. Security Implementation

Network Security

Security Groups (Stateful Firewall):

sg-alb-public: Port 443 (HTTPS) from 0.0.0.0/0, Port 80 (HTTP redirect) from 0.0.0.0/0
sg-eks-nodes: Port 443 from ALB SG, inter-node communication (all ports from same SG), ephemeral ports for outbound responses
sg-aurora-db: Port 5432 from EKS nodes SG and Lambda SG only
sg-elasticache: Port 6379 from EKS nodes SG only
sg-opensearch: Port 443 from EKS nodes SG only
sg-lambda: Outbound to databases, SQS, DynamoDB (no inbound rules)

Network ACLs (Stateless Subnet Protection):

Public subnets: Allow inbound 443, 80; allow ephemeral ports (1024-65535) for responses
Private app subnets: Allow all traffic from public subnets; deny direct internet inbound
Private data subnets: Allow traffic only from app subnets; deny all internet traffic

AWS WAF Rules:

AWS Managed Core Rule Set: SQL injection, XSS, LFI protection
Rate-based rule: 2000 requests per 5 minutes per IP, temporary block for 10 minutes
Geo-blocking: Block traffic from high-risk countries
IP reputation list: Block known malicious IPs (updated daily)
Size constraint: Block requests with body >8KB to prevent DoS
Custom rule: Block requests without valid JWT token for authenticated endpoints

VPC Flow Logs:

Enabled on VPC with ALL traffic capture
Stored in S3 with 90-day retention
Athena queries for security analysis and threat hunting

IAM Roles and Policies (Least Privilege)

Service Accounts (EKS Pod Identities):

Each microservice has dedicated IAM role via IRSA (IAM Roles for Service Accounts)
Booking service role: arn:aws:iam::ACCOUNT:role/booking-service-role
- Permissions: DynamoDB PutItem/GetItem on booking tables, SQS SendMessage to booking queue, SNS Publish to notification topic
User service role: Limited to Cognito, DynamoDB user tables, S3 profile images bucket

Lambda Execution Roles:

Separate role per Lambda function with minimal permissions
Example: Image processor role has S3 GetObject (source bucket), S3 PutObject (processed bucket), no broad S3:* permissions

Human Access:

No long-term access keys; SSO via AWS IAM Identity Center
MFA mandatory for console access and sensitive operations
Break-glass role for emergency access with CloudTrail alerts

Cross-Service Access:

Aurora enhanced monitoring role: Limited to CloudWatch PutMetricData
CodeBuild role: ECR push, S3 artifact access (build artifacts bucket only)

Data Encryption

At-Rest Encryption:

Aurora PostgreSQL: Encrypted with customer-managed KMS key aurora-cmk, automatic key rotation enabled
DynamoDB: Encryption at rest using AWS-managed keys (transparent), considering CMK for sensitive tables
S3: Server-side encryption with SSE-KMS using bucket-specific CMK, enforced via bucket policy denying unencrypted uploads
EBS volumes: All EKS node volumes encrypted with default KMS key
ElastiCache: At-rest encryption enabled with CMK
OpenSearch: Encryption at rest via KMS

In-Transit Encryption:

All inter-service communication via TLS 1.3
Aurora: SSL/TLS enforced via rds.force_ssl=1 parameter
ElastiCache: TLS mode enabled on all connections
Load balancers: HTTPS listeners with TLS 1.2+ only, SSL certificate from ACM
Kafka (MSK): TLS encryption for broker communication and client connections

Field-Level Encryption:

CloudFront field-level encryption for sensitive form data (credit cards, SSN)
Application-level encryption for PII using AWS Encryption SDK before storage

Secrets Management

AWS Secrets Manager:

Database credentials with automatic rotation every 30 days
API keys for third-party services (payment gateways, email providers)
JWT signing keys rotated quarterly
VPC-hosted secret rotation Lambda functions

EKS Secrets:

External Secrets Operator syncs from Secrets Manager to Kubernetes secrets
Sealed Secrets for GitOps (secrets encrypted in Git, decrypted in cluster)
Never commit plaintext secrets to repositories

Compliance Considerations

Standards:

PCI-DSS Level 1 (payment card data handling)
SOC 2 Type II (security, availability, confidentiality)
GDPR compliance (EU user data protection)

Controls:

Data residency: EU user data stored in eu-west-1 region only
Right to erasure: Automated data deletion workflow
Audit logging: All data access logged to CloudTrail (3-year retention)
Encryption: All data encrypted at rest and in transit
Access controls: MFA, least privilege, regular access reviews

DDoS Protection Strategy

AWS Shield Advanced:

Layer 3/4 DDoS protection with 24/7 DRT (DDoS Response Team) access
Cost protection against infrastructure scaling during attacks
Real-time attack notifications via SNS

Application Layer Protection:

WAF rate limiting and bot detection
CloudFront geo-blocking and origin shield
Auto-scaling to absorb volumetric attacks (cost implications monitored)

Monitoring:

CloudWatch metrics for anomalous traffic patterns
GuardDuty findings for reconnaissance and DDoS attempts
Automated alarms trigger incident response runbooks ***

6. Well-Architected Framework Alignment

Operational Excellence

Infrastructure as Code: All infrastructure provisioned via Terraform with GitOps workflow; changes peer-reviewed before merge; immutable infrastructure pattern

Monitoring & Observability: CloudWatch dashboards for 50+ metrics, Grafana for application-level insights, X-Ray for distributed tracing with service maps; alerting via PagerDuty with on-call rotation

Automation: CI/CD pipeline fully automated from commit to production; automated scaling policies; self-healing with health checks and pod restarts; chaos engineering with LitmusChaos for resilience testing

Runbooks & Playbooks: Documented incident response procedures for common scenarios (DB failover, cache invalidation, traffic spike); quarterly disaster recovery drills

Security

Identity & Access Management: IAM roles with least privilege; IRSA for pod-level permissions; MFA enforced; no long-term credentials; audit logs retained 3 years

Detective Controls: GuardDuty for threat detection; Security Hub for compliance posture (CIS Benchmarks); VPC Flow Logs analyzed for anomalies; CloudTrail for API auditing

Infrastructure Protection: Multi-layer defense (WAF, Shield, Security Groups, NACLs); private subnets for data tier; bastion host with session manager for admin access; regular vulnerability scanning with AWS Inspector

Data Protection: Encryption at rest (KMS CMK) and in transit (TLS 1.3); secrets rotation every 30 days; field-level encryption for PII; backup encryption; data classification (public, internal, confidential, restricted)

Incident Response: Automated playbooks for common incidents; isolation procedures for compromised instances; forensic capabilities with EBS snapshots and memory dumps

Reliability

Fault Isolation: Multi-AZ architecture with 3 AZs; Aurora failover <30s; stateless application design; bulkheads between services prevent cascading failures

Change Management: Blue-green deployments with traffic shifting; automated rollback on error rate >1%; canary releases for high-risk changes; feature flags for gradual rollout

Failure Handling: Exponential backoff with jitter for retries; circuit breakers (Hystrix pattern) prevent cascading failures; graceful degradation (serve cached results when DB unavailable); timeout budgets on all network calls

Backup Strategy: Aurora PITR (35 days), DynamoDB PITR (35 days), EKS Velero backups, S3 versioning with cross-region replication; tested restore procedures quarterly

Self-Healing: EKS pod restarts, ALB health checks, Lambda automatic retries, Aurora automatic failover, auto-scaling based on health metrics

Performance Efficiency

Right-Sizing: Graviton2 instances (r6g, c6g) for 20% better price-performance; right-sized databases based on CloudWatch metrics; Lambda memory optimization for cost/performance balance

Caching Strategy: Multi-tier caching (CloudFront edge, ElastiCache L2, DynamoDB DAX L3); cache hit ratio >85%; appropriate TTLs per data freshness requirements

CDN Usage: CloudFront with 450+ edge locations; origin shield reduces origin load; static asset optimization (Gzip, Brotli compression); image optimization (WebP format, lazy loading)

Database Optimization: Read replicas for read-heavy workloads; connection pooling (PgBouncer) to handle 10K+ connections; query optimization with EXPLAIN ANALYZE; database indexes on frequently queried columns

Asynchronous Processing: Event-driven architecture with Kafka/EventBridge; SQS for decoupling; Lambda for background jobs; batch processing for reports

Cost Optimization

Resource Optimization: EC2 Spot instances for 70% of non-critical workloads (development, batch jobs); Compute Savings Plans for 30% discount on steady-state compute; Reserved Instances for Aurora (3-year, 40% discount)

Storage Optimization: S3 Intelligent-Tiering automatically moves objects to cost-effective tiers; lifecycle policies archive logs to Glacier after 90 days; EBS gp3 instead of io2 for cost savings

Serverless & Managed Services: Lambda on-demand pricing (pay per invocation); DynamoDB on-demand for unpredictable traffic; Aurora Serverless v2 for development environments

Monitoring & Alerts: AWS Cost Explorer with anomaly detection; budget alerts at 80% threshold; resource tagging for cost allocation; monthly FinOps reviews identify optimization opportunities

Architecture Efficiency: Microservices scale independently (don't over-provision); auto-scaling policies prevent idle resources; scheduled scaling for predictable patterns (scale down nights/weekends)

Estimated Monthly Savings:

Spot instances: \$15,000/month
Savings Plans: \$8,000/month
S3 lifecycle policies: \$3,000/month
Right-sizing recommendations: \$5,000/month

Sustainability

Resource Efficiency: Graviton2 instances consume 60% less energy per workload; auto-scaling prevents idle resource waste; Lambda pay-per-use model eliminates idle compute

Regional Selection: Primary region us-east-1 has renewable energy commitments; consideration for AWS regions with lower carbon intensity

Minimal Idle Resources: Auto-scaling down to minimum thresholds during low traffic; scheduled shutdown of non-production environments outside business hours; DynamoDB on-demand eliminates provisioned idle capacity

Data Lifecycle: Automated deletion of obsolete data; compression for logs and backups; deduplication in S3 with intelligent tiering

Monitoring: Carbon footprint tracking via AWS Customer Carbon Footprint Tool; sustainability KPIs in executive dashboards

7. Deployment Flow

Step-by-Step Deployment Process

Phase 1: Infrastructure Provisioning (Terraform)

Initialize Terraform backend: S3 bucket + DynamoDB lock table
Deploy networking layer: VPC, subnets, route tables, NAT gateways, security groups
Deploy security layer: KMS keys, IAM roles, Secrets Manager secrets
Deploy data layer: Aurora cluster, DynamoDB tables, ElastiCache cluster, OpenSearch
Deploy compute layer: EKS cluster, Lambda functions, ALB
Deploy monitoring: CloudWatch dashboards, alarms, SNS topics
Deploy storage: S3 buckets with policies, EFS file system
Output: Terraform state stored in S3, infrastructure endpoints available

Phase 2: Kubernetes Setup

Configure kubectl with EKS cluster credentials
Install core add-ons: AWS Load Balancer Controller, EBS CSI driver, EFS CSI driver
Install monitoring stack: Prometheus, Grafana, metrics-server
Install security tools: Falco, OPA Gatekeeper
Configure IRSA (IAM Roles for Service Accounts) for each microservice
Create namespaces: production, staging, monitoring, ingress

Phase 3: ArgoCD Setup (GitOps)

Install ArgoCD in argocd namespace
Connect to GitHub repositories (infrastructure, applications)
Create ArgoCD Applications for each microservice
Configure sync policies: automated sync, self-heal, prune
Enable notifications to Slack for deployment status

Phase 4: Application Deployment

Developer commits code to GitHub feature branch
GitHub Actions triggered: Lint → Unit tests → Integration tests → Security scan (Trivy)
Merge to main branch triggers build phase
CodeBuild builds Docker images, tags with Git commit SHA and semantic version
Push images to Amazon ECR with vulnerability scanning
Update Kubernetes manifests in GitOps repository with new image tags
ArgoCD detects manifest changes, syncs to EKS cluster
Blue-green deployment: New version deployed alongside old version

Phase 5: Traffic Shifting & Validation

New pods pass readiness probes (HTTP GET /health returns 200)
Smoke tests executed against blue environment (new version)
Traffic gradually shifted: 10% → 25% → 50% → 100% over 30 minutes
Monitor key metrics during shift: Error rate <0.1%, p99 latency <500ms, throughput stable
If metrics breach thresholds, automatic rollback to green (old version)
If validation passes, 100% traffic to blue, green pods terminated after 1-hour soak period

CI/CD Pipeline Architecture

┌─────────────┐     ┌──────────────┐     ┌─────────────┐     ┌──────────┐
│   GitHub    │────▶│GitHub Actions│────▶│ CodeBuild   │────▶│   ECR    │
│  (Source)   │     │ (CI Pipeline)│     │(Docker Build)│     │(Registry)│
└─────────────┘     └──────────────┘     └─────────────┘     └────┬─────┘
                           │                                        │
                           │                                        │
                    ┌──────▼────────┐                              │
                    │  Test Suite   │                              │
                    │ • Unit Tests  │                              │
                    │ • Integration │                              │
                    │ • Security    │                              │
                    │   Scan (Trivy)│                              │
                    └───────────────┘                              │
                                                                   │
┌────────────────────────────────────────────────────────────────▼────┐
│                         GitOps Repository                            │
│  • Kubernetes manifests (YAML)                                      │
│  • Helm charts                                                       │
│  • Kustomize overlays (dev, staging, prod)                          │
│  • Image tags updated by CI pipeline                                │
└────────────────────────────────────┬────────────────────────────────┘
                                     │
                                     │
                            ┌────────▼─────────┐
                            │     ArgoCD       │
                            │  (CD Pipeline)   │
                            │ • Auto-sync      │
                            │ • Self-heal      │
                            │ • Health checks  │
                            └────────┬─────────┘
                                     │
                                     │
                      ┌──────────────▼──────────────┐
                      │       Amazon EKS            │
                      │ • Blue-Green Deployment     │
                      │ • Progressive Traffic Shift │
                      │ • Automated Rollback        │
                      └─────────────────────────────┘

Pipeline Stages:

Source: GitHub webhook triggers on push/PR
Lint: ESLint (Node.js), Checkstyle (Java), Black (Python)
Test: Jest (unit), Testcontainers (integration), 80% code coverage required
Security Scan: Trivy (images), SonarQube (code quality), Snyk (dependencies)
Build: Multi-stage Docker builds, layer caching, image size optimization
Push: ECR with immutable tags, vulnerability scan on push
Update Manifests: Automated PR to GitOps repo with new image tag
Deploy: ArgoCD syncs, blue-green strategy with Argo Rollouts
Verify: Smoke tests, metric validation, canary analysis
Promote/Rollback: Automatic decision based on success criteria

Blue-Green Deployment Strategy

Implementation with Argo Rollouts:

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: booking-service
spec:
  replicas: 10
  strategy:
    blueGreen:
      activeService: booking-service-active
      previewService: booking-service-preview
      autoPromotionEnabled: false  # Manual approval for prod
      scaleDownDelaySeconds: 3600  # Keep old version 1 hour
      prePromotionAnalysis:
        templates:
        - templateName: success-rate
        - templateName: latency-check
  template:
    spec:
      containers:
      - name: booking-service
        image: ECR_REPO/booking-service:NEW_TAG

Traffic Shifting:

Minute 0: Deploy blue (new version), green (old version) at 100% traffic
Minute 5: Blue at 10% traffic, validate error rate <0.1%, p99 <500ms
Minute 10: Blue at 25% traffic, compare metrics blue vs green
Minute 15: Blue at 50% traffic, full feature validation
Minute 25: Blue at 75% traffic, monitor for 5 minutes
Minute 30: Blue at 100% traffic, green on standby
Minute 90: Terminate green pods if no issues detected

Rollback Procedures

Automated Rollback Triggers:

Error rate >0.5% for 2 consecutive minutes
p99 latency >1000ms for 3 minutes
5xx response rate >1% sustained
Custom metric breach (booking success rate <99%)

Rollback Execution:

ArgoCD detects metric breach via Prometheus queries
Traffic immediately shifted back to green (old version)
Blue pods scaled down to 0
Incident created in PagerDuty, on-call engineer notified
Post-incident review scheduled within 24 hours

Manual Rollback:

kubectl argo rollouts abort booking-service -n production
kubectl argo rollouts undo booking-service -n production

Database Rollback (Complex):

Backward-compatible schema migrations prevent need for rollback
If required, restore from Aurora PITR to specific timestamp
Coordinated application + database rollback tested in staging

8. Monitoring & Operations

Key Metrics to Monitor

Application Metrics:

Metric	Threshold	Action
HTTP 5xx error rate	>0.5% for 2 min	Alert P1, investigate immediately
HTTP 4xx error rate	>5% for 5 min	Alert P2, check for API changes
API p50 latency	>200ms	Alert P3, investigate caching
API p99 latency	>500ms	Alert P2, check database queries
API p99.9 latency	>2000ms	Alert P1, potential outage
Request throughput	<50% of baseline	Alert P2, traffic drop investigation
Booking success rate	<99%	Alert P1, critical business impact
Search result latency	>100ms	Alert P3, OpenSearch performance
Payment success rate	<99.5%	Alert P1, revenue impact

Infrastructure Metrics:

Metric	Threshold	Action
EKS node CPU utilization	>80% for 5 min	Auto-scale nodes, alert P3
EKS node memory utilization	>85% for 3 min	Auto-scale nodes, alert P2
Pod restart count	>3 restarts in 10 min	Alert P2, check logs
Aurora CPU utilization	>75% sustained	Alert P2, consider scaling
Aurora connections	>80% of max	Alert P2, check connection pooling
Aurora replica lag	>1 second	Alert P3, check replication
DynamoDB throttled requests	>0	Alert P2, increase capacity
ElastiCache cache hit rate	<80%	Alert P3, review cache strategy
ElastiCache evictions	>100/min	Alert P2, increase cache size
OpenSearch cluster status	Red	Alert P1, potential data loss
OpenSearch JVM memory	>85%	Alert P2, heap size tuning
S3 4xx errors	>1% of requests	Alert P3, permission issues
ALB target response time	>500ms	Alert P2, investigate backends
ALB unhealthy host count	>0	Alert P2, check target health

Business Metrics:

Metric	Threshold	Action
Bookings per minute	<80% of forecast	Alert P2, potential issue
Property search queries	Sudden 50% drop	Alert P1, investigate search
User registration rate	<50% of baseline	Alert P3, check signup flow
Average booking value	-20% deviation	Alert P3, pricing review
Cancellation rate	>5%	Alert P2, check service quality

Alerting Thresholds

Severity Levels:

P1 (Critical): Immediate page to on-call, <15 min response, customer-impacting
P2 (High): Slack alert + email, <1 hour response, potential customer impact
P3 (Medium): Email alert, <4 hours response, operational concern
P4 (Low): Dashboard notification, next business day, informational

Alert Routing:

P1 alerts → PagerDuty (voice call + SMS) → On-call engineer
P2 alerts → Slack #incidents channel + PagerDuty (push notification)
P3 alerts → Slack #monitoring channel + Email
P4 alerts → Dashboard annotation only

On-Call Rotation:

24/7 coverage with 1-week shifts
Primary and secondary on-call engineers
Automatic escalation after 5 minutes if no acknowledgment

Log Aggregation Strategy

Centralized Logging Architecture:

Microservices → Fluent Bit (DaemonSet) → CloudWatch Logs → S3 Archive
                                        ↘
                                       OpenSearch for search/analysis

Log Categories:

Application Logs: INFO/WARN/ERROR from microservices, structured JSON format
Access Logs: ALB logs (HTTP requests, response codes, latency), S3 access logs
Audit Logs: CloudTrail (API calls), Database audit logs (connection, query logs)
Security Logs: VPC Flow Logs, WAF logs, GuardDuty findings

Log Retention:

CloudWatch Logs: 90 days (operational queries)
S3 Archive: 7 years (compliance, compressed with Gzip)
OpenSearch: 30 days (fast search and analysis)

Log Format (Structured JSON):

{
  "timestamp": "2025-12-11T20:09:00.000Z",
  "level": "ERROR",
  "service": "booking-service",
  "pod": "booking-service-7d8f9c-abc12",
  "trace_id": "1-5f8a2b3c-4d5e6f7g8h9i0j1k",
  "user_id": "usr_123456",
  "booking_id": "bkg_789012",
  "error_type": "DatabaseConnectionError",
  "message": "Failed to acquire connection from pool",
  "stack_trace": "...",
  "context": {
    "db_host": "aurora-cluster.xyz.us-east-1.rds.amazonaws.com",
    "retry_count": 3
  }
}

Log Analysis Queries:

Error trend analysis by service
P99 latency per endpoint
User journey tracking via trace_id
Security anomaly detection (failed auth attempts, unusual access patterns)

Dashboard Requirements

Executive Dashboard (Business KPIs):

Real-time bookings per minute (line chart)
Total daily revenue (gauge)
Active users (current count)
Conversion funnel: Searches → Views → Bookings (sankey diagram)
Geographic distribution (map visualization)
Top performing properties (table)
System health score (composite metric: availability × performance)

Operations Dashboard (Infrastructure):

Cluster health: Node count, pod count, resource utilization
Database performance: CPU, connections, replication lag, IOPS
Cache metrics: Hit rate, evictions, memory usage
API performance: Request rate, latency percentiles, error rate
Cost tracker: Daily spend by service (EC2, RDS, data transfer)

Service-Specific Dashboards:

Booking Service: Booking rate, success rate, payment failures, step function executions
Search Service: Query rate, OpenSearch latency, cache hit rate, result relevance score
User Service: Registrations, logins, profile updates, Cognito metrics
Notification Service: Email/SMS sent, delivery rate, bounce rate, queue depth

SLA Dashboard:

Availability: 99.99% target (43.2 minutes downtime/month allowed)
Latency: p99 <500ms target
Error rate: <0.1% target
Time to resolution: P1 incidents resolved <1 hour

Incident Response Workflow

Detection Phase:

Alert triggered by CloudWatch/Prometheus alarm
PagerDuty creates incident, pages on-call engineer
Automated enrichment: Recent deployments, similar past incidents, runbook links

Response Phase:

On-call acknowledges alert within 5 minutes (or escalates to secondary)
Join incident Slack channel (auto-created: #incident-YYYY-MM-DD-NNN)
Execute initial triage runbook: Check dashboards, review logs, assess blast radius
Declare severity: SEV1 (critical, all hands), SEV2 (major), SEV3 (minor)
For SEV1: Page incident commander, create Zoom bridge, notify leadership

Mitigation Phase:

Implement immediate mitigation: Rollback deployment, scale resources, failover region
Monitor key metrics for improvement
Update incident channel every 15 minutes with status
External communication if customer-facing (status page update)

Resolution Phase:

Validate all metrics returned to normal
Monitor for 30 minutes to ensure stability
Mark incident as resolved in PagerDuty
Schedule post-incident review within 24 hours

Post-Incident Review:

Blameless postmortem document
Timeline of events with metric screenshots
Root cause analysis (5 Whys methodology)
Action items with owners and due dates
Runbook updates to prevent recurrence

9. Cost Estimation

Monthly Cost Breakdown - Development Environment

Service	Configuration	Units	Unit Cost	Monthly Cost
Compute
EKS Control Plane	Per cluster	1	\$73	\$73
EC2 (m6i.large nodes)	8 vCPU, 32GB	3 nodes	\$0.096/hr × 730hr	\$210
Lambda	1GB, 100K invocations	-	\$0.20/1M + compute	\$50
Database
Aurora PostgreSQL	db.r6g.large	1 instance	\$0.26/hr × 730hr	\$190
DynamoDB	On-demand, 10GB	-	\$1.25/GB + requests	\$30
ElastiCache	cache.r6g.large	1 node	\$0.252/hr × 730hr	\$184
Storage
S3 Standard	100GB	-	\$0.023/GB	\$2.30
EBS gp3	200GB total	-	\$0.08/GB	\$16
Networking
ALB	1 ALB	-	\$0.0225/hr × 730hr	\$16.43
NAT Gateway	1 NAT	-	\$0.045/hr × 730hr	\$32.85
Data Transfer	50GB out	-	\$0.09/GB	\$4.50
Monitoring
CloudWatch	Logs, metrics	-	-	\$30
Total Dev Environment				~\$839/month

Monthly Cost Breakdown - Production Environment

Service	Configuration	Units	Unit Cost	Monthly Cost
Compute
EKS Control Plane	Per cluster	1	\$73	\$73
EC2 On-Demand	m6i.2xlarge	10 nodes	\$0.384/hr × 730hr	\$2,803
EC2 Spot Instances	m6i.2xlarge, 70% discount	20 nodes	\$0.115/hr × 730hr	\$1,679
Lambda	1M invocations, 2GB avg	-	Compute charges	\$350
Fargate	4 vCPU, 8GB tasks	5 tasks	\$0.12/hr × 730hr	\$438
Database
Aurora PostgreSQL (Primary)	db.r6g.4xlarge	1 writer	\$1.04/hr × 730hr	\$759
Aurora Read Replicas	db.r6g.2xlarge	2 replicas	\$0.52/hr × 730hr × 2	\$759
Aurora Storage	500GB, I/O	-	\$0.10/GB + I/O	\$150
Aurora Cross-Region	2 regions	2 replicas	\$0.52/hr × 730hr × 2	\$759
DynamoDB	On-demand, 200GB	-	\$1.25/GB + 10M writes	\$450
ElastiCache Redis	cache.r6g.xlarge	9 nodes (3×3)	\$0.503/hr × 730hr × 9	\$3,303
ElastiCache Global	Cross-region	6 nodes	\$0.503/hr × 730hr × 6	\$2,203
OpenSearch	r6g.2xlarge.search	9 nodes total	\$0.524/hr × 730hr × 9	\$3,442
OpenSearch Storage	4.5TB EBS gp3	-	\$0.08/GB × 4500	\$360
Storage
S3 Standard	5TB	5000GB	\$0.023/GB	\$115
S3 Intelligent-Tiering	10TB	10000GB	\$0.021/GB avg	\$210
S3 Glacier	20TB archive	20000GB	\$0.004/GB	\$80
S3 Requests	GET/PUT	-	-	\$50
EBS gp3	3TB total (nodes)	3000GB	\$0.08/GB	\$240
EFS	100GB	-	\$0.30/GB	\$30
Networking
ALB	2 ALBs	-	\$0.0225/hr × 730hr × 2	\$32.85
NLB (internal)	1 NLB	-	\$0.0225/hr × 730hr	\$16.43
NAT Gateway	3 NAT (per AZ)	-	\$0.045/hr × 730hr × 3	\$98.55
NAT Data Processing	5TB	5000GB	\$0.045/GB	\$225
CloudFront	10TB transfer	-	\$0.085/GB avg	\$850
CloudFront Requests	100M requests	-	\$0.0075/10K	\$75
Route 53	Hosted zone, queries	-	-	\$50
Data Transfer Out	15TB inter-region	15000GB	\$0.02/GB	\$300
Security
WAF	Web ACL, rules	-	\$5 + \$1/rule × 10	\$15
Shield Advanced	DDoS protection	1	\$3,000	\$3,000
Secrets Manager	50 secrets	-	\$0.40/secret	\$20
GuardDuty	Data analyzed	-	-	\$50
Messaging
Amazon MSK	kafka.m5.2xlarge	6 brokers	\$0.42/hr × 730hr × 6	\$1,839
MSK Storage	2TB EBS per broker	12TB	\$0.10/GB	\$1,200
SQS	100M requests	-	\$0.40/1M	\$40
SNS	10M notifications	-	\$0.50/1M	\$5
EventBridge	50M events	-	\$1/1M	\$50
Monitoring & Operations
CloudWatch Logs	500GB ingestion	-	\$0.50/GB	\$250
CloudWatch Metrics	Custom metrics	-	\$0.30/metric	\$150
CloudWatch Alarms	100 alarms	-	\$0.10/alarm	\$10
X-Ray	10M traces	-	\$5/1M	\$50
CloudTrail	Multi-region	1 trail	-	\$2
CI/CD
CodeBuild	1000 build mins	-	\$0.005/min	\$5
ECR Storage	500GB images	-	\$0.10/GB	\$50
Additional Services
Cognito	100K MAU	-	\$0.0055/MAU (>50K)	\$275
SES	100K emails	-	\$0.10/1K	\$10
Step Functions	10K executions	-	\$0.025/1K	\$0.25
Backup & DR
Automated Backups	Aurora, DynamoDB	-	-	\$200
S3 Cross-Region Replication	2TB/month	-	\$0.02/GB	\$40
Total Production Environment				~\$28,050/month

Cost Optimization Recommendations

Immediate Savings (0-30 days):

Compute Savings Plans (3-year): Commit to \$1,500/month compute usage → Save 40% (\$7,200/year)
Aurora Reserved Instances (1-year): Reserve db.r6g instances → Save 35% (\$10,000/year)
S3 Lifecycle Policies: Auto-tier infrequently accessed data → Save \$1,500/month
Right-size EKS Nodes: Analyze CPU/memory usage, downsize over-provisioned nodes → Save \$800/month
Remove Unused EBS Snapshots: Automated cleanup of snapshots >90 days → Save \$300/month

Total Immediate Savings: ~\$4,100/month (\$49,200/year)

Medium-Term Optimizations (30-90 days):

Increase Spot Instance Usage: Expand to 80% spot for stateless workloads → Save \$600/month
ElastiCache Reserved Nodes: 3-year commitment → Save 45% (\$1,800/month)
CloudFront Optimization: Enable Brotli compression, optimize cache hit rate to 95% → Save \$200/month
Database Query Optimization: Reduce Aurora I/O by 40% through query tuning → Save \$500/month
Lambda Memory Optimization: Right-size Lambda memory allocations → Save \$150/month

Total Medium-Term Savings: ~\$3,250/month (\$39,000/year)

Long-Term Strategies (90+ days):

Multi-Region Optimization: Evaluate actual DR usage, consider active-active vs warm standby → Potential \$3,000/month
Graviton3 Migration: Upgrade to Graviton3 instances for 25% better price-performance → Save \$800/month
Aurora Serverless v2: Use for non-production environments → Save \$400/month
Data Archival Strategy: Aggressive archival to Glacier Deep Archive → Save \$500/month

Total Long-Term Savings: ~\$4,700/month (\$56,400/year)

Total Optimized Production Cost: ~\$28,050 - \$12,050 = \$16,000/month

Cost Allocation Tags

Environment: production | staging | development
Service: booking | search | user | payment | notification
Team: platform | backend | data | security
CostCenter: engineering | infrastructure | security
Project: booking-platform-v2

Monthly Cost Summary

Environment	Original Cost	Optimized Cost	Annual Cost (Optimized)
Development	\$839	\$600	\$7,200
Staging	\$3,500	\$2,500	\$30,000
Production (Primary)	\$28,050	\$16,000	\$192,000
Production (DR Regions)	\$8,000	\$5,000	\$60,000
Total	\$40,389/month	\$24,100/month	\$289,200/year

10. Implementation Roadmap

Phase 1: Foundation (Weeks 1-4)

Week 1-2: Infrastructure Setup

Set up AWS organization, accounts (prod, staging, dev), consolidated billing
Configure IAM Identity Center for SSO, create baseline IAM roles
Establish Terraform repository structure, initialize remote state backend
Deploy networking layer: VPC, subnets, NAT gateways, security groups across 3 AZs
Configure Route 53 hosted zones, register SSL certificates in ACM
Deliverable: Base infrastructure in dev environment, Terraform modules documented

Week 3-4: Security & Compliance Foundation

Deploy KMS customer-managed keys for encryption
Configure AWS Config rules for compliance monitoring
Enable CloudTrail multi-region trail, GuardDuty, Security Hub
Set up Secrets Manager with initial secrets (placeholders)
Implement baseline IAM policies and service roles
Configure VPC Flow Logs to S3
Deliverable: Security baseline passing CIS Benchmark, compliance dashboard

Phase 2: Data Layer (Weeks 5-7)

Week 5: Database Provisioning

Deploy Aurora PostgreSQL cluster with Multi-AZ configuration
Set up automated backups, point-in-time recovery
Create database schemas, apply initial migrations
Configure connection pooling (PgBouncer)
Deliverable: Aurora cluster operational with connection from bastion host

Week 6: NoSQL & Caching

Deploy DynamoDB tables with on-demand capacity
Configure DynamoDB streams for event processing
Deploy ElastiCache Redis cluster in cluster mode
Set up DAX cluster for DynamoDB acceleration
Deploy OpenSearch cluster with master/data node separation
Deliverable: All data stores provisioned, basic CRUD operations tested

Week 7: Data Integration

Configure cross-region replication: Aurora Global Database, DynamoDB Global Tables
Set up MSK (Kafka) cluster with initial topics
Deploy data migration scripts for existing data (if applicable)
Performance testing: Database load tests, cache hit rate validation
Deliverable: Data layer achieving RTO/RPO targets, cross-region replication validated

Phase 3: Compute & Application Layer (Weeks 8-12)

Week 8-9: EKS Cluster Setup

Deploy EKS cluster with managed node groups
Install core add-ons: ALB controller, EBS CSI, EFS CSI, Cluster Autoscaler
Configure IRSA for pod-level IAM permissions
Deploy monitoring stack: Prometheus, Grafana with initial dashboards
Set up internal ALB for service mesh communication
Deliverable: EKS cluster operational with demo application deployed

Week 10-11: Microservices Deployment

Containerize all microservices with multi-stage Docker builds
Create Helm charts for each service with configurable values
Deploy services in dev environment: User, Property, Booking, Search, Payment, Notification
Configure service-to-service authentication (JWT, mTLS)
Implement health check endpoints, readiness/liveness probes
Deliverable: All microservices deployed, inter-service communication validated

Week 12: Serverless Components

Deploy Lambda functions for event processing, image processing, scheduled jobs
Configure API Gateway for external API access (if needed)
Set up Step Functions for booking workflow orchestration
Deploy SQS queues, SNS topics for async communication
Configure EventBridge rules for event routing
Deliverable: Event-driven architecture functional, end-to-end booking flow operational

Phase 4: CI/CD & GitOps (Weeks 13-14)

Week 13: CI Pipeline

Set up GitHub Actions workflows: Lint, test, security scan, build
Configure CodeBuild for Docker image builds
Create ECR repositories with lifecycle policies
Integrate Trivy for container vulnerability scanning
Set up SonarQube for code quality gates
Deliverable: Automated CI pipeline from commit to ECR push

Week 14: CD Pipeline

Install ArgoCD in EKS cluster
Create GitOps repository structure with Kustomize overlays
Configure ArgoCD applications for all microservices
Implement blue-green deployment strategy with Argo Rollouts
Set up automated rollback triggers based on CloudWatch metrics
Deliverable: GitOps-based CD pipeline with automated deployments

Phase 5: Observability & Operations (Weeks 15-16)

Week 15: Monitoring & Alerting

Configure CloudWatch dashboards: Executive, Operations, Service-specific
Create CloudWatch alarms for critical metrics (50+ alarms)
Set up PagerDuty integration with on-call schedules
Deploy X-Ray for distributed tracing
Configure log aggregation with Fluent Bit to CloudWatch Logs
Deliverable: Complete observability stack, on-call rotation active

Week 16: Operational Readiness

Document runbooks for common incidents (DB failover, cache invalidation, etc.)
Create incident response procedures, postmortem templates
Conduct tabletop disaster recovery exercise
Performance testing: Load tests simulating 10K concurrent users
Chaos engineering: Pod deletion, AZ failure simulation with LitmusChaos
Deliverable: Operations team trained, runbooks validated through simulations

Phase 6: Performance & Optimization (Weeks 17-18)

Week 17: Performance Tuning

Database optimization: Query analysis with EXPLAIN, index creation
Cache warming strategies, cache invalidation patterns
CDN configuration: CloudFront distribution with optimal TTLs
API optimization: Response compression, pagination, rate limiting
OpenSearch index optimization, query tuning
Deliverable: Performance targets met (p99 <500ms, 99.99% availability)

Week 18: Cost Optimization

Implement Savings Plans and Reserved Instance purchases
Configure S3 lifecycle policies for automatic tiering
Right-size EKS nodes based on actual usage patterns
Enable Spot instance auto-scaling groups
Set up AWS Cost Explorer with budget alerts
Deliverable: 30% cost reduction achieved, FinOps dashboard operational

Phase 7: Multi-Region & DR (Weeks 19-20)

Week 19: Secondary Region Deployment

Deploy infrastructure to eu-west-1 and ap-southeast-1 using Terraform
Configure cross-region replication for all data stores
Set up Route 53 health checks and failover routing
Deploy warm standby (10% capacity) in secondary regions
Deliverable: Multi-region architecture operational, data replication validated

Week 20: DR Testing

Execute full disaster recovery drill: Primary region failure simulation
Validate RTO/RPO targets through actual failover
Test data integrity after cross-region promotion
Document lessons learned, update DR procedures
Conduct security audit, penetration testing
Deliverable: DR capabilities proven, security audit passed

Phase 8: Go-Live Preparation (Weeks 21-22)

Week 21: Production Hardening

Enable AWS Shield Advanced for DDoS protection
Configure WAF rules tuned to production traffic patterns
Implement rate limiting, bot detection
Set up real user monitoring (RUM) for frontend performance
Conduct final security review, compliance validation
Deliverable: Production environment hardened, compliance certifications obtained

Week 22: Go-Live & Hypercare

Execute blue-green cutover from legacy system (if applicable)
Gradual traffic migration: 10% → 50% → 100% over 1 week
24/7 war room during initial launch week
Monitor key metrics continuously, rapid iteration on issues
Collect user feedback, prioritize post-launch improvements
Deliverable: Production launch successful, system stable under load

Post-Launch: Continuous Improvement (Ongoing)

Month 2-3:

Feature velocity optimization: Reduce deployment time, increase release frequency
Advanced observability: Implement SLIs, SLOs, error budgets
Cost optimization sprint: Identify and eliminate waste
Performance benchmarking against competitors

Month 4-6:

Multi-region active-active deployment for global scale
Advanced ML/personalization features leveraging real-time data
Platform engineering: Self-service infrastructure for developers
Automated remediation for common incidents

Critical Path Items

Weeks 1-4: Infrastructure foundation (blocker for all subsequent work)
Weeks 5-7: Data layer (prerequisite for application deployment)
Weeks 8-12: Application layer (core product functionality)
Weeks 15-16: Observability (required for production readiness)
Week 20: DR validation (compliance requirement for launch)

Team Skill Requirements

Platform/Infrastructure Team (3-4 engineers):

AWS Solutions Architect certification (minimum Associate, preferred Professional)
Strong Terraform/IaC experience (2+ years)
Kubernetes administration (CKA certification preferred)
Networking fundamentals (VPC, subnets, routing, load balancing)
Security best practices (IAM, encryption, compliance)

Backend Development Team (6-8 engineers):

Proficiency in Node.js, Java, Python, or Go
Microservices architecture patterns
Database design (SQL and NoSQL)
API design (RESTful, gRPC)
Event-driven architecture experience

DevOps/SRE Team (2-3 engineers):

CI/CD pipeline design and implementation
GitOps methodologies (ArgoCD experience preferred)
Observability tools (Prometheus, Grafana, CloudWatch)
Incident response and on-call experience
Chaos engineering practices

Security Engineer (1-2 engineers):

AWS security services (IAM, KMS, WAF, GuardDuty)
Compliance frameworks (PCI-DSS, SOC 2, GDPR)
Container security, vulnerability management
Security automation and policy-as-code

Data Engineer (1-2 engineers):

Database administration (PostgreSQL, DynamoDB)
Data pipeline design (Kafka, streaming)
Performance optimization and query tuning
Backup and recovery procedures

Migration Strategy (If Applicable)

Pre-Migration Phase:

Data assessment: Volume, relationships, dependencies
Application inventory: Services, APIs, integrations
Define migration waves by service criticality

Migration Approach: Strangler Fig Pattern

Deploy new platform alongside legacy system
Implement API gateway routing: New users → new platform, existing users → legacy
Gradual data synchronization: Bidirectional sync during transition period
Feature parity validation: Ensure all legacy features available in new platform
Traffic cutover: Incrementally route users to new platform (10% weekly increases)
Legacy decommission: After 100% traffic migrated and 30-day soak period

Data Migration:

Use AWS Database Migration Service (DMS) for continuous replication
Validation: Row counts, checksum comparisons, sample data verification
Rollback plan: DNS cutover back to legacy if critical issues detected

11. Assumptions & Prerequisites

Traffic/User Load Assumptions

Daily Active Users (DAU): 10 million users
Peak Concurrent Users: 500,000 simultaneous connections
API Request Rate: 100,000 requests/second (peak), 30,000 req/sec (average)
Booking Rate: 5,000 bookings/minute during peak hours
Search Queries: 50,000 searches/minute
User Session Duration: Average 15 minutes
Geographic Distribution: 40% North America, 35% Europe, 20% Asia, 5% other regions
Traffic Pattern: 3x daily peak vs off-peak, 2x weekend vs weekday traffic
Seasonality: 5x traffic during holiday seasons (Dec, Jul-Aug)

Data Volume Assumptions

Property Listings: 5 million active properties, growing 10% annually
User Accounts: 50 million registered users, 20% active monthly
Bookings: 100 million bookings annually (8.3M per month)
Images: 50 million property images, 2-5 MB average size (150TB total)
Database Size: 2TB relational data (Aurora), 5TB NoSQL data (DynamoDB)
Log Volume: 500GB logs/day (CloudWatch), compressed to 50GB/day in S3
Search Index: 10GB OpenSearch indices for property search
Cache Memory: 150GB active dataset in ElastiCache
Event Throughput: 1 million events/second during peak (Kafka/EventBridge)

Availability Requirements

Target SLA: 99.99% uptime (43.2 minutes downtime/month)
RTO (Recovery Time Objective): <1 hour for complete region failure
RPO (Recovery Point Objective): <5 minutes for transactional data
Maintenance Windows: No planned downtime; rolling updates only
Regional Failover: Automatic DNS failover in <2 minutes
Service Dependencies: Third-party payment gateway 99.95% SLA, email service 99.9% SLA

Performance Requirements

API Latency: p50 <100ms, p99 <500ms, p99.9 <2000ms
Search Latency: <100ms for property search results
Booking Confirmation: <3 seconds end-to-end (including payment processing)
Page Load Time: <2 seconds for initial page load (including CDN caching)
Database Query Performance: >95% of queries <50ms
Cache Hit Rate: >85% for frequently accessed data
CDN Cache Hit Rate: >90% for static assets

Required Team Expertise

AWS Certifications: Minimum 2 team members with AWS Solutions Architect Professional
Kubernetes Experience: CKA or equivalent for platform team
Programming Proficiency: Senior-level developers with 5+ years experience in Node.js/Java/Python
DevOps Tools: Hands-on experience with Terraform, ArgoCD, GitHub Actions
Database Skills: PostgreSQL DBA with performance tuning experience
Security Clearance: Security team member with relevant certifications (CISSP, CEH preferred)
On-Call Capability: Team members available for 24/7 rotation

Existing Infrastructure Considerations

Greenfield Deployment: No legacy infrastructure dependencies
Domain & DNS: Existing domain with Route 53 management
SSL Certificates: ACM used for certificate provisioning and renewal
Corporate Network: VPN connectivity to AWS VPC for admin access (optional)
Identity Provider: Existing SSO provider integration with AWS IAM Identity Center
Compliance: No existing compliance certifications; will pursue PCI-DSS, SOC 2 post-launch

Budget Constraints

Infrastructure Budget: \$25,000-30,000/month for production (aligns with estimates)
Tooling Budget: \$10,000/month for third-party tools (Datadog, PagerDuty, etc.)
Team Budget: 15-20 FTE engineers for 6-month implementation
Professional Services: \$50,000 budget for AWS Professional Services engagement (architecture review)
Training: \$5,000/year per engineer for certifications and training

Regulatory & Compliance

Data Residency: GDPR compliance requires EU data stored in EU region only
PCI-DSS: Level 1 compliance required for payment processing (tokenization strategy)
Data Retention: 7-year retention for financial records, 90-day for operational logs
Right to Erasure: GDPR right to be forgotten implementation required
Audit Trails: Immutable audit logs for all data access and modifications
Privacy Policy: Updated to reflect AWS data processing agreements

Third-Party Integrations

Payment Gateway: Stripe/Braintree integration for payment processing
Email Service: Amazon SES for transactional emails, SendGrid backup
SMS Gateway: Amazon SNS with Twilio fallback
Analytics: Google Analytics, Mixpanel for user behavior tracking
Customer Support: Zendesk/Intercom integration for support tickets
Fraud Detection: Third-party fraud detection API (Sift, Forter)

12. Risks & Mitigations

Technical Risks

Risk 1: Database Connection Pool Exhaustion

Likelihood: High during traffic spikes
Impact: Critical - API errors, booking failures
Mitigation:
- Implement PgBouncer connection pooling with 10,000 max connections
- Configure application-level connection pools (HikariCP for Java, Sequelize for Node.js)
- Auto-scaling read replicas based on connection count metric
- Circuit breaker pattern to prevent cascading failures
- Monitoring alert when connections >80% capacity

Risk 2: DynamoDB Throttling

Likelihood: Medium during unpredictable traffic bursts
Impact: High - User session failures, degraded experience
Mitigation:
- On-demand capacity mode for unpredictable tables (user-sessions, user-signals)
- DAX caching layer reduces direct DynamoDB reads by 70%
- Exponential backoff with jitter for retried requests
- Monitoring throttled request metrics with P1 alerts
Alternative: Pre-provision capacity with auto-scaling during known peak periods

Risk 3: Multi-Region Replication Lag

Likelihood: Medium during network issues or high write volume
Impact: High - Data inconsistency, double bookings in secondary region
Mitigation:
- Aurora Global Database replication typically <1s; monitor lag metric closely
- Implement application-level conflict resolution for rare conflicts
- Booking transactions only in primary region (write single-region pattern)
- Secondary regions read-only until manual promotion during DR
- Quarterly DR drills validate data consistency post-failover

Risk 4: Kafka Message Loss

Likelihood: Low with MSK, but possible during broker failures
Impact: High - Lost user events, incomplete analytics, missed notifications
Mitigation:
- Kafka replication factor 3 (data replicated to 3 brokers)
- Producer acknowledgment: acks=all (wait for all replicas)
- Consumer groups with committed offsets prevent duplicate processing
- Dead-letter queue for failed message processing
- Idempotent consumers handle duplicate messages gracefully

Risk 5: Kubernetes Control Plane Outage

Likelihood: Very low (AWS manages EKS control plane with 99.95% SLA)
Impact: Critical - Cannot deploy, scale, or manage pods
Mitigation:
- Existing pods continue running during control plane outage
- HPA and Cluster Autoscaler have local caching; continue operating briefly
- Multi-region deployment provides redundancy
- AWS support escalation for rapid resolution
- Post-incident review with AWS TAM to understand root cause

Operational Risks

Risk 6: Insufficient On-Call Coverage

Likelihood: Medium - Engineer burnout, attrition
Impact: High - Delayed incident response, SLA breaches
Mitigation:
- Primary and secondary on-call rotation (1-week shifts)
- Follow-the-sun model with global team (if applicable)
- Automated runbook execution for common incidents (reduces manual toil)
- Compensation: On-call stipend + overtime pay
- Regular retrospectives to improve on-call experience

Risk 7: Deployment-Induced Outages

Likelihood: Medium during frequent deployments
Impact: High - Service downtime, customer complaints
Mitigation:
- Blue-green deployments with automated validation gates
- Canary analysis: Gradual traffic shifting (10% → 100% over 30 min)
- Automated rollback on error rate >0.5% or latency >1000ms
- Deployment freeze during peak traffic periods (Fri-Sun)
- Post-deployment monitoring: 30-minute soak period before marking success

Risk 8: Security Breach or Data Leak

Likelihood: Low with proper controls, but high-impact
Impact: Critical - Legal liability, reputation damage, GDPR fines
Mitigation:
- Defense-in-depth: WAF, Security Groups, NACLs, encryption
- Regular penetration testing (quarterly) by third-party security firm
- GuardDuty and Security Hub continuous monitoring with automated response
- Secrets rotation every 30 days, no hardcoded credentials
- Incident response plan with legal and PR coordination
- Cyber insurance policy for breach liability coverage

Business Risks

Risk 9: Cost Overruns

Likelihood: High without proper governance
Impact: Medium - Budget overages, reduced profitability
Mitigation:
- AWS Budget alerts at 80%, 100%, 120% thresholds
- Monthly FinOps reviews with finance and engineering teams
- Rightsizing recommendations enforced through automation
- Savings Plans and Reserved Instances for predictable workloads
- Cost allocation tags for chargeback to product teams
- Automatic shutdown of non-production environments outside business hours

Risk 10: Third-Party Service Outages

Likelihood: Medium - Payment gateway, email service, fraud detection
Impact: High - Lost bookings, revenue impact
Mitigation:
- Multi-vendor strategy: Primary and backup providers (Stripe + Braintree)
- Circuit breaker pattern: Fail fast on third-party timeouts
- Graceful degradation: Queue bookings for later processing if payment gateway down
- SLA monitoring with vendor escalation paths
- Regular vendor reviews and performance assessments

Risk 11: Skill Gaps in Team

Likelihood: Medium - AWS/Kubernetes expertise scarce
Impact: Medium - Delayed implementation, suboptimal architecture
Mitigation:
- Hiring: Prioritize candidates with AWS certifications and K8s experience
- Training: \$5,000/year per engineer for certifications (AWS SA Pro, CKA)
- AWS Professional Services engagement for architecture review (\$50K)
- Knowledge sharing: Weekly tech talks, internal documentation wiki
- Pair programming and code reviews for knowledge transfer

Alternative Approaches Considered

Alternative 1: Serverless-First Architecture (Lambda + API Gateway)

Pros: Lower operational overhead, automatic scaling, pay-per-use pricing
Cons: Cold start latency (200-500ms), 15-minute Lambda timeout limit, vendor lock-in
Decision: Hybrid approach - Use Lambda for event processing, EKS for core services requiring <100ms latency

Alternative 2: Multi-Cloud (AWS + GCP/Azure)

Pros: Vendor diversification, leverage best-of-breed services per cloud
Cons: Increased operational complexity, higher costs, team skill dilution
Decision: Single-cloud (AWS) for simplicity; revisit multi-cloud if vendor risk increases

Alternative 3: Self-Managed Kubernetes (EC2 with kubeadm)

Pros: Full control, cost savings (~30% vs EKS)
Cons: Operational burden (control plane management, upgrades, security patches)
Decision: Managed EKS for reduced operational overhead; focus engineering on product features

Alternative 4: Monolithic Architecture

Pros: Simpler deployment, easier debugging, lower latency for inter-component calls
Cons: Limited scalability, tight coupling, difficult to parallelize development
Decision: Microservices for independent scaling and team autonomy; accept increased operational complexity

Alternative 5: Relational-Only Database (No DynamoDB)

Pros: Simpler data model, ACID transactions across all data
Cons: Aurora limited to 15 read replicas, higher latency for key-value lookups
Decision: Polyglot persistence - Aurora for transactional data requiring ACID, DynamoDB for high-throughput key-value access patterns (sessions, user signals)

This comprehensive architecture provides a production-ready, scalable, secure, and cost-optimized solution for a high-performance travel booking platform following AWS Well-Architected Framework principles. The design handles 10M+ daily active users with 99.99% availability, sub-500ms latency, and robust disaster recovery capabilities.