Apache Kafka is not just a message broker. It was initially designed and implemented by LinkedIn in order to serve as a message queue. Since 2011, Kafka has been open sourced and quickly evolved into a distributed streaming platform, which is used for the implementation of real-time data pipelines and streaming applications.
It is horizontally scalable, fault-tolerant, wicked fast, and runs in
production in thousands of companies.
Modern organisations have various data pipelines that facilitate the communication between systems or services. Things get a bit more complicated when a reasonable number of services needs to communicate with each other at real time.
The architecture becomes complex since various integrations are required in order to enable the inter-communication of these services. More precisely, for an architecture that encompasses m source and n target services, n x m distinct integrations need to be written. Also, every integration comes with a different specification, meaning that one might require a different protocol (HTTP, TCP, JDBC, etc.) or a different data representation (Binary, Apache Avro, JSON, etc.), making things even more challenging. Furthermore, source services might address increased load from connections that could potentially impact latency.
Apache Kafka leads to more simple and manageable architectures, by decoupling data pipelines. Kafka acts as a high-throughput distributed system where source services push streams of data, making them available for target services to pull them at real-time.
Also, a lot of open-source and enterprise-level User Interfaces for managing Kafka Clusters are available now. For more details refer to my answer to this question.
You can find more details about Apache Kafka and how it works in the blog post "Why Apache Kafka?"
