Real-Time Data Processing

# Real-Time Data Processing

From fraud detection to live dashboards, real-time processing is essential for modern applications.

## Stream Processing Fundamentals

**Windows**

- Tumbling: Fixed, non-overlapping intervals

- Sliding: Overlapping intervals

- Session: Activity-based grouping

**State Management**

Maintain state across events for aggregations and joins.

## Architecture Patterns

**Lambda Architecture**

Combine batch and stream processing. Best of both worlds.

**Kappa Architecture**

Stream processing only. Simpler but requires careful design.

## Technologies

- Apache Kafka: Event streaming platform

- Apache Flink: Stateful stream processing

- Kafka Streams: Lightweight library

- AWS Kinesis: Managed streaming

## Challenges

- Exactly-once processing

- Late-arriving data

- State consistency

- Scalability

Real-time insights require real-time infrastructure.