Step Snap 1: [Kafka Topics & Messages - The Building Blocks]

💡 Inside Kafka: Topics, Events, and Messages

In Kafka, everything flows through topics - the central concept for organizing streaming data:

Producer → Topic → Consumer
           ↑
        Events

🌊 What is a Topic?

A topic is a continuous stream of events flowing through the system. Think of it like a specialized channel on TV that broadcasts only one type of content.

📦 What are Events?

Events are individual data points at specific times:

• 🌡️ Example: Room temperature readings every 30 seconds
• 🕒 "At 2:15 PM, the temperature was 32°C"
• 🕓 "At 2:45 PM, the temperature was 33°C"

📝 Anatomy of a Kafka Message

Each event contains a message with three key components:

┌─────────────────────────────────┐
│ MESSAGE                         │
├─────────────┬─────────┬─────────┤
│ Key         │ Value   │ Timestamp│
├─────────────┼─────────┼─────────┤
│ Used for    │ The     │ When    │
│ partitioning│ actual  │ this    │
│ and routing │ data    │ happened│
└─────────────┴─────────┴─────────┘

📚 Logs: How Kafka Stores Data

Unlike traditional databases that use B-trees, Kafka stores data as logs - simple append-only sequences of events. This makes writing extremely fast and efficient.

⏱️ Remember: Near Real-Time, Not Immediate

Kafka provides near real-time processing with:

• Messages stored for a configurable retention period
• Multiple consumers can read the same message