Technical Specification: Real-Time Data Processing Pipeline
Version: 2.1.0
Last Updated: November 2024

1. Overview
The Real-Time Data Processing Pipeline (RTDPP) is designed to handle high-volume data streams from multiple sources, process them in real-time, and deliver insights to downstream systems with minimal latency.

2. Architecture Components

2.1 Data Ingestion Layer
- Apache Kafka for message queuing
- Support for 10,000+ messages per second
- Multi-topic architecture with partitioning
- Built-in data validation and schema registry

2.2 Processing Engine
- Apache Spark Streaming for real-time processing
- Micro-batch processing with 1-second intervals
- Stateful transformations with checkpointing
- Custom UDFs for business logic

2.3 Storage Layer
- Primary: Apache Cassandra for time-series data
- Secondary: PostgreSQL for metadata
- Archive: S3 for long-term storage
- Redis for caching frequently accessed data

3. Data Flow
1. Data sources publish to Kafka topics
2. Spark Streaming consumes from Kafka
3. Data transformation and enrichment
4. Results written to Cassandra and PostgreSQL
5. Real-time alerts sent via WebSocket
6. Batch exports to S3 every hour

4. Performance Requirements
- Latency: < 100ms end-to-end
- Throughput: 1M events per minute
- Availability: 99.9% uptime
- Data retention: 30 days hot, 1 year cold

5. Security Measures
- TLS 1.3 for all data in transit
- AES-256 encryption for data at rest
- OAuth 2.0 for API authentication
- Role-based access control (RBAC)
- Audit logging for all operations

6. Monitoring and Alerting
- Prometheus for metrics collection
- Grafana for visualization
- PagerDuty for incident management
- Custom alerts for SLA breaches

7. Deployment
- Kubernetes orchestration
- Docker containers for all services
- Blue-green deployment strategy
- Auto-scaling based on load

8. Dependencies
- Java 11+ for Spark applications
- Python 3.8+ for data processing scripts
- Node.js 16+ for API services
- Minimum 16GB RAM per processing node

9. API Endpoints
- POST /api/v2/ingest - Data ingestion
- GET /api/v2/stream/{id} - Real-time stream
- GET /api/v2/metrics - System metrics
- POST /api/v2/query - Ad-hoc queries

10. Future Enhancements
- Machine learning integration for anomaly detection
- GraphQL API for flexible queries
- Multi-region deployment for disaster recovery
- Support for additional data formats (Avro, Parquet)