Apache Kafka was created at LinkedIn in 2011 by Jay Kreps, Neha Narkhede, and Jun Rao to solve a specific problem: LinkedIn needed a unified platform to handle the massive volume of real-time data flowing between its systems. The three founders later left to start Confluent, the commercial company behind Kafka.
Kafka functions as a distributed commit log — producers write events to topics, and consumers read from those topics. The design is simple but powerful. Events are immutable and ordered within partitions, persisted to disk with configurable retention, and can be consumed by multiple independent consumers without interference.
The platform handles extraordinary throughput. Major deployments process millions of events per second with low-millisecond latency. LinkedIn’s own Kafka installation processes over 7 trillion messages per day across its clusters.
Kafka Streams and ksqlDB bring stream processing capabilities directly into the Kafka ecosystem. Instead of needing a separate framework like Spark Streaming or Flink, you can write stream processing logic that runs on Kafka itself. Kafka Connect provides a standardized way to move data between Kafka and external systems.
The technology has become infrastructure at most large tech companies. Uber uses Kafka to coordinate rides in real time. Netflix uses it for event processing across its microservices. Banks use it for fraud detection and transaction processing.
Kafka’s influence on software architecture has been profound. It popularized event-driven architecture patterns and the concept of the “log” as a fundamental data structure. The idea that every change in a system should be captured as an immutable event has reshaped how modern distributed systems are designed.
