Apache Kafka

Apache Kafka is a high-performance data streaming platform used by thousands of companies for various applications and use cases.


  • Message broker
  • Operational monitoring data handling
  • Log aggregation solution
  • Data processing pipelines


  • Scalability: Kafka is highly scalable and can handle a large volume of data and thousands of messages per second.
  • Fault Tolerance: It provides built-in replication and fault-tolerance, ensuring that data is not lost in case of node failure.
  • High Throughput: Kafka can handle high throughput and is known for its ability to efficiently process a large number of messages.
  • Real-Time Stream Processing: Its architecture allows for real-time stream processing and can support real-time analytics use cases.
  • Durability: Data in Kafka is persisted to disk, making it durable and suitable for reliable message storage.
  • Integration Flexibility: It is widely supported and integrates well with various data processing frameworks.
  • Reliability: Kafka provides strong durability guarantees and message delivery semantics.
  • Decoupling of Systems: It enables decoupling of data streams and provides a reliable and fault-tolerant communication layer between systems.

Disadvantages & Concerns

  • Complexity: Setting up and maintaining Kafka can be complex, especially when dealing with clustered environments and high availability configurations.
  • Operational Overhead: Requires operational expertise in managing and configuring the Kafka clusters.
  • Learning Curve: Developers and operators may require time to understand Kafka’s architecture and concepts.
  • Storage Cost: While durability is an advantage, it also increases storage costs due to persistent data storage.
  • Integration Challenges: Although it integrates with many systems, integration can be challenging in some cases, especially with legacy systems.
  • Monitoring and Management: Effective monitoring and management of a Kafka cluster can be resource-intensive.