What is Apache Kafka?

Apache Kafka is an open-source distributed event streaming platform developed by Apache Software Foundation. It’s designed for high-performance, real-time data pipelines and streaming applications.
At its core, Kafka is like a messaging system — similar to RabbitMQ or ActiveMQ — but it’s much faster, scalable, and fault-tolerant.
It helps different parts of a system communicate asynchronously by sending messages (called events) between producers and consumers via a central Kafka cluster.

Key Components

Component	Description
Producer	Sends (writes/publishes) data to Kafka topics.
Consumer	Reads (subscribes) data from Kafka topics.
Topic	A category or feed name where messages are stored.
Partition	A topic is divided into partitions for parallel processing and scalability.
Broker	A Kafka server that stores data and serves clients.
Cluster	A group of brokers working together.
Zookeeper (legacy)	Used to manage metadata and coordination (recent versions replaced with KRaft).

Why Kafka is Powerful

1. High Throughput – Handles millions of messages per second.
2. Scalable – Add more brokers easily without downtime.
3. Durable – Stores messages on disk; supports data replication.
4. Fault Tolerant – Automatically handles node failures.
5. Real-Time Processing – Works well with stream processing tools (e.g., Kafka Streams, Flink, Spark).
6. Decouples Systems – Producers and consumers don’t depend on each other directly.

Use Cases

1. Log aggregation – Collect logs from multiple services.
2. Event sourcing – Store and replay all system events.
3. Real-time analytics – Process data streams for dashboards or alerts.
4. Data pipeline – Move data between microservices or systems (e.g., DB → Kafka → Elasticsearch).
5. Integration – Connect multiple systems like payment, order, and inventory in e-commerce apps.

Example Flow

Producer → Topic (Partition 1, 2, 3) → Consumer Group

Example:

1. A banking system sends “Transaction Created” events to a Kafka topic.
2. One microservice consumes them to update account balance.
3. Another service consumes them to send notifications.

Both read the same event independently — no duplication in logic.

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Apache Kafka Architecture

              ┌────────────────────────┐
              │       Producers        │
              │ (send messages/events) │
              └────────────┬───────────┘
                           │
                           ▼
              ┌────────────────────────┐
              │        Topics          │
              │  (split into partitions)│
              └────────────┬───────────┘
                           │
           ┌───────────────┼────────────────┐
           ▼               ▼                ▼
    ┌────────────┐   ┌────────────┐   ┌────────────┐
    │ Partition 0│   │ Partition 1│   │ Partition 2│
    │ (Broker 1) │   │ (Broker 2) │   │ (Broker 3) │
    └────────────┘   └────────────┘   └────────────┘
           │               │                │
           ▼               ▼                ▼
 ┌──────────────────────────────────────────────┐
 │                 Consumers                    │
 │ (read messages in Consumer Groups)           │
 └──────────────────────────────────────────────┘

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Apache Kafka vs RabbitMQ

Both Kafka and RabbitMQ are messaging systems, but they’re built for different purposes.

Feature	Apache Kafka	RabbitMQ
Type	Distributed event streaming platform	Traditional message broker
Message Model	Publish–Subscribe (stream-based)	Queue-based (push-based)
Use Case Focus	Real-time data streaming and event processing	Reliable message delivery between services
Message Retention	Messages are stored for a configurable time (not deleted after consumption)	Messages are removed after consumption
Consumer Model	Consumers pull messages when ready	Broker pushes messages to consumers
Ordering Guarantee	Maintains order within a partition	Can lose order if multiple consumers
Throughput	Extremely high (millions of messages/sec)	Moderate (depends on configuration)
Durability	Persistent, replicated logs (very reliable)	Persistent queues (reliable but slower)
Scalability	Highly scalable with partitions and brokers	Limited horizontal scaling
Latency	Low (real-time streaming)	Slightly higher (broker routing overhead)
Acknowledgment	Offset-based commit (manual or automatic)	Message acknowledgment (ACK/NACK)
Typical Use Cases	– Event-driven microservices

Quick Summary

• Use Kafka when you need real-time event streaming, analytics, and scalability.
• Use RabbitMQ when you need simple, reliable task/message delivery between systems.