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Apache Kafka

Apache Kafka has been a popular mention in my journey as a developer, particularly in the realm of real-time data streaming and distributed systems. Having recently completed a project using Kafka on an AWS EC2 instance, I find myself reflecting on its core concepts and practical applications.

Refreshing its Core Concepts

Apache Kafka operates on a distributed architecture consisting primarily of brokers, producers, consumers, and ZooKeeper.

  1. Brokers: These are the Kafka servers responsible for storing and managing the streams of records.
  2. Producers: Applications that publish data to Kafka topics.
  3. Consumers: Applications that subscribe to and process data from Kafka topics.
  4. ZooKeeper: Coordinates and manages Kafka brokers to ensure reliability and scalability.

Its Unique Features

  • Scalability: Kafka's distributed nature allows horizontal scaling, accommodating increasing data volumes seamlessly.
  • Fault Tolerance: Replication across Kafka brokers ensures data durability and availability, critical for mission-critical applications.
  • High Throughput aka "Fast": Kafka's ability to handle millions of messages per second makes it ideal for scenarios requiring real-time data processing.
  • Data Integration: Acts as a central hub for integrating different data sources and systems, facilitating efficient data pipelines.

Comparison with others

After getting more exposure to different technology like RabbitMQ, I can't help but to ask a question

Why Kafka for Real-Time Streaming?

In the landscape of messaging systems, Kafka offers distinct advantages over traditional message queues (MQ). Unlike traditional MQ systems that focus on point-to-point communication, Kafka excels in scenarios demanding:

  • Publish-Subscribe Model: Allows multiple consumers to subscribe to the same data stream concurrently, supporting diverse use cases from analytics to monitoring.
  • Event Sourcing: Facilitates capturing and storing events as they occur, enabling applications to replay events for analysis or recovery.
  • Microservices Architecture: Supports asynchronous communication between microservices, enhancing decoupling and scalability.

End Note

As I delve deeper into Apache Kafka, my goal is to explore further and document its pros and cons across different use cases.

©2024 Chantelle Loh