Graph Database Knowledge Graph

Neptune graph database with knowledge graph construction, relationship modeling, fraud detection, recommendation engine, and graph algorithms

📋 Table of Contents

• Overview
• Architecture
• Features
• Getting Started
• Deployment
• Monitoring
• Security
• Cost
• License

🎯 Overview

Neptune graph database with knowledge graph construction, relationship modeling, fraud detection, recommendation engine, and graph algorithms

Key Capabilities

• ✅ High availability and fault tolerance
• ✅ Auto-scaling and cost optimization
• ✅ Security best practices built-in
• ✅ Production-ready monitoring and alerting
• ✅ Infrastructure as Code automation
• ✅ Multi-environment support (dev/staging/prod)

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🏗️ Architecture

System Architecture

graph TB
    subgraph Internet["User/Client Layer"]
        Users[End Users<br/>Applications]
    end
    
    subgraph Edge["Edge/Entry Layer"]
        API[API Gateway<br/>Load Balancer]
        CDN[CloudFront CDN<br/>Edge Locations]
        WAF[AWS WAF<br/>Protection]
    end
    
    subgraph Compute["Compute Layer"]
        Lambda[Lambda Functions<br/>Serverless]
        ECS[ECS Fargate<br/>Containers]
        EC2[EC2 Auto Scaling<br/>Instances]
    end
    
    subgraph Data["Data Layer"]
        RDS[(RDS/Aurora<br/>Database)]
        S3[(S3 Buckets<br/>Object Storage)]
        DDB[(DynamoDB<br/>NoSQL)]
        Cache[ElastiCache<br/>Redis/Memcached]
    end
    
    subgraph Monitoring["Observability Stack"]
        CW[CloudWatch<br/>Metrics & Logs]
        XRay[X-Ray<br/>Distributed Tracing]
        SNS[SNS<br/>Alerting]
    end
    
    Users --> CDN
    Users --> API
    CDN --> API
    WAF --> API
    
    API --> Lambda
    API --> ECS
    API --> EC2
    
    Lambda --> DDB
    Lambda --> S3
    ECS --> RDS
    EC2 --> Cache
    
    Compute -.->|Metrics| CW
    Compute -.->|Traces| XRay
    CW -->|Alarms| SNS
    
    style Internet fill:#E3F2FD
    style Edge fill:#C8E6C9
    style Compute fill:#FFF9C4
    style Data fill:#FFE0B2
    style Monitoring fill:#F3E5F5

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Data Flow Sequence

sequenceDiagram
    participant User
    participant API as API Gateway
    participant Auth as Authentication
    participant Compute as Compute Layer
    participant Cache as Cache Layer
    participant Database as Database
    participant Monitor as Monitoring
    
    User->>API: API Request
    API->>Auth: Verify Token
    Auth-->>API: Token Valid
    
    API->>Compute: Process Request
    Compute->>Cache: Check Cache
    
    alt Cache Hit
        Cache-->>Compute: Return Cached Data
    else Cache Miss
        Compute->>Database: Query Data
        Database-->>Compute: Return Data
        Compute->>Cache: Update Cache
    end
    
    Compute-->>API: Response Data
    API-->>User: API Response
    
    Compute->>Monitor: Send Metrics & Logs
    Monitor->>Monitor: Check Thresholds
    
    alt Alert Condition
        Monitor->>User: Send Alert
    end

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Deployment Pipeline

graph LR
    Dev[Development<br/>Local] -->|Git Push| CI[CI/CD Pipeline<br/>GitHub Actions]
    CI -->|Build & Test| Build[Build Stage<br/>Compile/Package]
    Build -->|Unit Tests| Test[Test Stage<br/>Integration Tests]
    
    Test -->|Deploy| Staging[Staging Environment<br/>Pre-Production]
    Staging -->|Manual Approval| Canary[Canary Deployment<br/>10% Traffic]
    
    Canary -->|Monitor| Decision{Health Check<br/>Pass?}
    Decision -->|Yes| Prod50[50% Traffic]
    Decision -->|No| Rollback[Auto Rollback<br/>Previous Version]
    
    Prod50 -->|Monitor| Prod100[100% Production<br/>Full Deployment]
    
    style Dev fill:#E3F2FD
    style Staging fill:#FFF3E0
    style Canary fill:#FFE0B2
    style Prod100 fill:#C8E6C9
    style Rollback fill:#FFCDD2

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Network Architecture

graph TB
    subgraph VPC["VPC (10.0.0.0/16)"]
        subgraph PublicSubnets["Public Subnets"]
            PubSub1[Public Subnet 1<br/>10.0.1.0/24<br/>us-east-1a]
            PubSub2[Public Subnet 2<br/>10.0.2.0/24<br/>us-east-1b]
            NAT1[NAT Gateway 1]
            NAT2[NAT Gateway 2]
        end
        
        subgraph PrivateSubnets["Private Subnets"]
            PrivSub1[Private Subnet 1<br/>10.0.11.0/24<br/>us-east-1a]
            PrivSub2[Private Subnet 2<br/>10.0.12.0/24<br/>us-east-1b]
        end
        
        subgraph DatabaseSubnets["Database Subnets"]
            DBSub1[DB Subnet 1<br/>10.0.21.0/24<br/>us-east-1a]
            DBSub2[DB Subnet 2<br/>10.0.22.0/24<br/>us-east-1b]
        end
        
        IGW[Internet Gateway]
        
        IGW --> PubSub1
        IGW --> PubSub2
        PubSub1 --> NAT1
        PubSub2 --> NAT2
        NAT1 --> PrivSub1
        NAT2 --> PrivSub2
        PrivSub1 -.->|No Direct Internet| DBSub1
        PrivSub2 -.->|No Direct Internet| DBSub2
    end
    
    Internet((Internet)) --> IGW
    
    style PublicSubnets fill:#C8E6C9
    style PrivateSubnets fill:#FFF9C4
    style DatabaseSubnets fill:#FFE0B2

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✨ Features

Core Features

1. High Availability

   • Multi-AZ deployment across availability zones
   • Automatic failover and health checks
   • 99.99% uptime SLA

2. Auto Scaling

   • Dynamic scaling based on CPU, memory, and custom metrics
   • Predictive scaling for anticipated load
   • Cost-optimized instance sizing

3. Security

   • End-to-end encryption (data at rest and in transit)
   • IAM roles with least privilege access
   • VPC isolation and security groups
   • AWS WAF for application protection
   • CloudTrail audit logging

4. Monitoring & Observability

   • CloudWatch dashboards with custom metrics
   • Real-time alerting via SNS/email
   • X-Ray distributed tracing
   • VPC Flow Logs for network monitoring
   • Automated anomaly detection

5. Cost Optimization

   • Right-sized EC2 instances
   • Spot instances for non-critical workloads
   • Auto-shutdown of dev/test environments
   • S3 Intelligent-Tiering
   • Reserved instances for predictable workloads

6. CI/CD Integration

   • Automated deployment pipeline
   • Infrastructure testing
   • Canary and blue/green deployments
   • Automatic rollback on failures

7. Disaster Recovery

   • Automated backups (daily/weekly)
   • Cross-region replication
   • Point-in-time recovery
   • RTO < 1 hour, RPO < 15 minutes

8. Compliance & Governance

   • AWS Config rules for compliance
   • Automated compliance reporting
   • Resource tagging strategy
   • Cost allocation tags

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🚀 Getting Started

Prerequisites

# AWS CLI
aws --version  # >= 2.13.0

# Configure AWS credentials
aws configure

# Infrastructure tool (depends on project)
# For CDK projects:
node --version     # >= 18.0.0
npm install -g aws-cdk

# For Terraform projects:
terraform version  # >= 1.5.0

# For Pulumi projects:
pulumi version     # >= 3.90.0

Quick Deploy

See ARCHITECTURE.md for detailed deployment instructions.

Development Environment:

# Deploy to dev
./deploy.sh dev

Production Environment:

# Deploy to production (requires approval)
./deploy.sh prod

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📊 Monitoring

CloudWatch Dashboards

• Infrastructure Metrics: CPU, memory, disk, network
• Application Metrics: Request count, latency, errors
• Custom Metrics: Business KPIs and domain-specific metrics

Alerts & Notifications

• High CPU usage (>80% for 5 minutes)
• High error rates (>1% of requests)
• Database connection issues
• Disk space warnings (>85% full)
• Cost anomalies

Distributed Tracing

• X-Ray integration for request tracing
• Service map visualization
• Latency analysis
• Error tracking

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🔐 Security

Security Best Practices

• ✅ Encryption: All data encrypted at rest (KMS) and in transit (TLS 1.3)
• ✅ IAM: Least privilege access with role-based permissions
• ✅ Network: VPC isolation, private subnets, security groups
• ✅ Secrets: AWS Secrets Manager for credentials
• ✅ Logging: CloudTrail for API audit logs
• ✅ Scanning: Automated security vulnerability scanning
• ✅ WAF: AWS WAF rules for common threats
• ✅ MFA: Multi-factor authentication required

Compliance

• HIPAA compliant infrastructure
• PCI-DSS ready architecture
• SOC 2 controls implemented
• GDPR data protection

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💰 Cost Estimation

Monthly Cost Breakdown

Component	Development	Production
Compute (EC2/ECS/Lambda)	$100-300	$500-2000
Database (RDS/DynamoDB)	$50-150	$200-1000
Storage (S3/EBS)	$20-50	$100-500
Networking (Data Transfer)	$10-30	$50-300
Monitoring (CloudWatch)	$10-20	$50-200
Total	$190-550	$900-4000

Cost Optimization Tips

• Use Spot instances for dev/test (70% savings)
• Enable S3 Intelligent-Tiering
• Set up auto-shutdown for non-prod environments
• Use Reserved Instances for predictable workloads
• Implement proper resource tagging

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📝 License

MIT License - see LICENSE file for details

🤝 Contributing

See CONTRIBUTING.md for contribution guidelines

👤 Author

Rahul Ladumor

• 🌐 Portfolio: acloudwithrahul.in
• 💼 GitHub: @rahulladumor
• 📧 Email: rahuldladumor@gmail.com
• 💬 LinkedIn: linkedin.com/in/rahulladumor

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Made with ❤️ by Rahul Ladumor