Data Pipeline (Kafka/MQTT for AI Events)

Role in the Project

Manages real-time data streaming between the AI engine, backend, and frontend.

Strengths & Weaknesses

Strengths:

• Kafka ensures reliable high-throughput streaming.
• MQTT is lightweight and efficient for IoT applications.

Weaknesses:

• Kafka requires careful infrastructure setup.
• MQTT has limited message retention.

Available Technologies & Comparison

• Kafka (Chosen for AI Event Processing) vs. RabbitMQ (Lower throughput) vs. Redis Streams (Less scalable for AI events).
• MQTT (Chosen for Edge AI Communication) vs. WebSockets (Persistent connection, higher bandwidth use).

Chosen Approach

• Kafka for event-driven AI workflows.
• MQTT for lightweight IoT communications.

Example of Kafka AI event schema:

{
  "event": "object_detected",
  "timestamp": 1714500000,
  "data": {
    "imageId": "12345",
    "objects": [
      { "label": "Person", "confidence": 0.98 },
      { "label": "Car", "confidence": 0.95 }
    ]
  }
}