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Add comprehensive documentation
- System architecture documentation - Quick start guide for 15-minute deployment - Streaming pipeline implementation guide - Documentation structure and navigation - Code examples and best practices
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‎docs/README.md‎

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# AWS Data Platform Documentation
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Welcome to the comprehensive documentation for the AWS Data Platform template. This documentation covers architecture, deployment, operations, and customization of the platform.
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## 📚 Documentation Structure
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### Architecture
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- [System Architecture](architecture/system-architecture.md) - High-level platform architecture
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- [Data Flow](architecture/data-flow.md) - Data ingestion and processing flows
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- [Security Architecture](architecture/security.md) - Security model and best practices
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- [Networking](architecture/networking.md) - VPC and network design
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### Deployment Guides
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- [Quick Start](guides/quick-start.md) - Get started in 15 minutes
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- [Production Deployment](guides/production-deployment.md) - Production deployment checklist
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- [Multi-Region Setup](guides/multi-region.md) - Deploy across multiple AWS regions
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- [Disaster Recovery](guides/disaster-recovery.md) - Backup and recovery procedures
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### Component Guides
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- [Streaming Pipeline](guides/streaming-pipeline.md) - Kinesis and Lambda configuration
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- [Batch Processing](guides/batch-processing.md) - EMR and Spark job development
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- [Data Warehouse](guides/data-warehouse.md) - Redshift optimization
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- [Machine Learning](guides/machine-learning.md) - SageMaker pipeline setup
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- [Business Intelligence](guides/business-intelligence.md) - QuickSight dashboard creation
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### Operations
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- [Monitoring](guides/monitoring.md) - CloudWatch dashboards and alerts
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- [Cost Optimization](guides/cost-optimization.md) - Reduce AWS costs
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- [Performance Tuning](guides/performance-tuning.md) - Optimize processing performance
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- [Troubleshooting](guides/troubleshooting.md) - Common issues and solutions
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### API Reference
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- [Lambda Functions](api/lambda-functions.md) - Stream processor API documentation
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- [Data Models](api/data-models.md) - Schema definitions
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- [REST APIs](api/rest-apis.md) - API Gateway endpoints
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## 🚀 Quick Links
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- [Environment Configuration](.env.example) - Environment variables reference
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- [Infrastructure Code](../infrastructure/) - CDK stack implementations
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- [Source Code](../src/) - Application source code
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- [Scripts](../scripts/) - Deployment and utility scripts
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## 💡 Getting Help
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- **Issues**: [GitHub Issues](https://github.com/tysoncung/aws-data-platform/issues)
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- **Discussions**: [GitHub Discussions](https://github.com/tysoncung/aws-data-platform/discussions)
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- **Examples**: See the [examples](examples/) directory
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## 📖 Additional Resources
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- [AWS Best Practices](https://aws.amazon.com/architecture/well-architected/)
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- [AWS CDK Documentation](https://docs.aws.amazon.com/cdk/)
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- [Apache Spark Documentation](https://spark.apache.org/docs/latest/)
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- [Amazon Redshift Documentation](https://docs.aws.amazon.com/redshift/)

‎docs/architecture/system-architecture.md‎

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# System Architecture
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## Overview
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The AWS Data Platform implements a modern, cloud-native architecture that combines real-time streaming, batch processing, and machine learning capabilities in a unified platform.
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## Architecture Principles
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### 1. Separation of Concerns
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- **Ingestion Layer**: Handles data intake from various sources
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- **Processing Layer**: Transforms and enriches data
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- **Storage Layer**: Persists data in appropriate formats
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- **Serving Layer**: Provides data to consumers
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- **Consumption Layer**: Enables analytics and ML workloads
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### 2. Scalability
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- Horizontal scaling for all components
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- Auto-scaling based on workload
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- Elastic compute resources
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### 3. Reliability
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- Multi-AZ deployments
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- Automated failover
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- Data replication and backups
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### 4. Security
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- Encryption at rest and in transit
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- IAM role-based access control
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- VPC isolation and security groups
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- AWS Secrets Manager for credentials
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## Component Architecture
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### Real-Time Streaming Layer
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```
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Data Sources → Kinesis Data Streams → Lambda Functions → DynamoDB/S3
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↓
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Kinesis Analytics → Real-time Dashboards
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↓
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Kinesis Firehose → S3 Data Lake
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```
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**Components:**
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- **Kinesis Data Streams**: Ingests streaming data at scale
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- **Lambda Functions**: Serverless processing of stream records
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- **DynamoDB**: Low-latency storage for real-time data
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- **Kinesis Analytics**: SQL queries on streaming data
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### Batch Processing Layer
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```
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S3 Data Lake → EMR Cluster → Processed Data → Redshift
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↓
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Glue ETL Jobs → Data Catalog
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↓
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Athena Queries → Analytics
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```
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**Components:**
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- **EMR**: Distributed processing with Spark/Hadoop
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- **Glue ETL**: Serverless data transformation
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- **Athena**: Interactive SQL queries on S3 data
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- **Redshift**: Data warehouse for analytics
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### Machine Learning Layer
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```
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Feature Store → SageMaker Training → Model Registry
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↓
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Model Endpoints → Inference API
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↓
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A/B Testing → Production
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```
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**Components:**
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- **SageMaker**: End-to-end ML platform
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- **Feature Store**: Centralized feature management
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- **Model Registry**: Version control for ML models
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- **Endpoints**: Real-time and batch inference
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## Data Flow Patterns
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### 1. Lambda Architecture
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Combines batch and streaming processing:
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- **Speed Layer**: Real-time processing via Kinesis/Lambda
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- **Batch Layer**: Historical processing via EMR/Glue
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- **Serving Layer**: Unified view via Redshift/DynamoDB
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### 2. Kappa Architecture
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Streaming-first approach:
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- All data flows through Kinesis
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- Reprocessing via stream replay
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- Simplified architecture with single processing path
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### 3. Data Mesh
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Domain-oriented decentralization:
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- Domain-specific data products
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- Self-serve data platform
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- Federated governance
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## Technology Stack
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### Core Services
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| Component | Technology | Purpose |
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|-----------|------------|---------|
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| Streaming | Amazon Kinesis | Real-time data ingestion |
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| Compute | AWS Lambda, EMR | Data processing |
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| Storage | S3, DynamoDB, Redshift | Data persistence |
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| Analytics | Athena, QuickSight | Data analysis |
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| ML | SageMaker | Machine learning |
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| Orchestration | Step Functions, Airflow | Workflow management |
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| Monitoring | CloudWatch, X-Ray | Observability |
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### Programming Languages
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- **Python**: Primary language for all components
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- **SQL**: Data transformations and queries
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- **Spark**: Large-scale data processing
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### Data Formats
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- **Raw**: JSON, CSV, Parquet
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- **Processed**: Parquet, ORC
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- **Serving**: JSON, Avro
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## Network Architecture
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### VPC Design
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```
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VPC (10.0.0.0/16)
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├── Public Subnets (10.0.1.0/24, 10.0.2.0/24)
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│ └── NAT Gateways, Load Balancers
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├── Private Subnets (10.0.10.0/24, 10.0.11.0/24)
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│ └── EMR, Lambda, ECS Tasks
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└── Database Subnets (10.0.20.0/24, 10.0.21.0/24)
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└── Redshift, RDS, ElastiCache
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```
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### Security Groups
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- **EMR-Master-SG**: Controls access to EMR master node
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- **EMR-Worker-SG**: Inter-node communication
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- **Redshift-SG**: Database access control
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- **Lambda-SG**: Outbound only for Lambda functions
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## High Availability
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### Multi-AZ Deployment
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- Redshift clusters span multiple AZs
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- DynamoDB global tables for cross-region replication
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- S3 cross-region replication for critical data
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### Disaster Recovery
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- **RPO**: 1 hour for batch data, real-time for streaming
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- **RTO**: 2 hours for full platform recovery
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- Automated backups and snapshots
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- Infrastructure as code for rapid redeployment
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## Performance Optimization
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### Caching Strategy
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- **CloudFront**: CDN for static content
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- **ElastiCache**: Redis for application caching
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- **S3 Intelligent-Tiering**: Automatic storage optimization
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### Data Partitioning
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- **S3**: Partitioned by year/month/day/hour
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- **Redshift**: Distribution and sort keys optimization
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- **DynamoDB**: Partition key design for even distribution
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## Cost Optimization
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### Resource Management
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- **Auto-scaling**: Scale based on actual usage
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- **Spot Instances**: For EMR worker nodes
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- **Reserved Instances**: For predictable workloads
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- **S3 Lifecycle Policies**: Archive old data to Glacier
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### Monitoring
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- **Cost Explorer**: Track spending trends
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- **Budgets**: Alert on cost overruns
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- **Trusted Advisor**: Optimization recommendations
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## Security Architecture
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### Data Protection
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- **Encryption**: AES-256 for data at rest
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- **TLS 1.2+**: For data in transit
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- **KMS**: Key management service integration
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### Access Control
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- **IAM Roles**: Service-specific permissions
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- **Lake Formation**: Fine-grained data access
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- **Secrets Manager**: Credential rotation
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### Compliance
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- **CloudTrail**: Audit logging
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- **Config**: Compliance monitoring
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- **GuardDuty**: Threat detection
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## Scalability Limits
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### Streaming
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- Kinesis: 1MB/sec or 1000 records/sec per shard
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- Lambda: 1000 concurrent executions (soft limit)
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- DynamoDB: 40,000 RCU/WCU per table
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### Batch Processing
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- EMR: 500 nodes per cluster
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- Glue: 100 DPU per job
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- Redshift: 128 nodes per cluster
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### Storage
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- S3: Unlimited storage
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- Single object: 5TB maximum
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- DynamoDB item: 400KB maximum

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