Kappa Architecture

1. Introduction

The Kappa Architecture is a data processing architecture designed to handle the complexities of real-time data processing while addressing the limitations of traditional architectures like Lambda Architecture. It simplifies data processing by using a single stream processing framework, primarily focused on event-driven architecture.

2. Key Concepts

• Event Sourcing: Captures state changes as events.
• Streaming Data: Processes data in real-time as it arrives.
• Single System: Combines batch and real-time processing into a unified system.

3. Architecture Flow

graph TD;
    A[Source Data] --> B[Stream Processing];
    B --> C[Real-time Analytics];
    C --> D[Data Storage];
    D --> E[Batch Processing];
    E --> B;
            

This flowchart illustrates the movement of data through the Kappa Architecture, starting from the source data to real-time analytics, storage, and back to batch processing.

4. Best Practices

1. Use a robust stream processing framework (e.g., Apache Kafka, Apache Flink).
2. Implement event sourcing for better state management.
3. Regularly monitor and fine-tune the system for performance.

5. FAQ