Glossary · Web & App Development
Apache Kafka
Overview
What Apache Kafka is
Apache Kafka is an open-source distributed event streaming platform, originally built at LinkedIn and now maintained by the Apache Software Foundation. It lets applications publish streams of events, store them durably, and read them later, acting as a central, replayable log that connects the systems producing data with the many systems that consume it.
How it works
Events are written to topics, which are split into partitions, append-only logs distributed across a cluster of brokers and replicated for durability. Producers write to topics; consumers read at their own pace by tracking an offset, and consumer groups share the load. Because events are retained, new consumers can replay history. Kafka Connect moves data in and out, and Kafka Streams processes it.
Why it matters for your business
Kafka is the backbone of event-driven architectures: real-time analytics, activity tracking, log aggregation, and decoupled microservices that react to events instead of calling each other directly. In backend and app backend/API development, a full-service team uses Kafka to move high volumes of data reliably between services and to feed real-time features and dashboards.
Where we use it
Related Zen in Tech services
How our team puts Apache Kafka to work in real projects.
FAQ
Apache Kafka — common questions
Apache Kafka vs RabbitMQ, what's the difference?
Kafka is a distributed log built for high-throughput, replayable event streams; RabbitMQ is a traditional message broker built for flexible routing and per-message delivery. Choose Kafka for large-scale streaming and analytics, and RabbitMQ for task queues and complex routing.
Is Apache Kafka hard to run?
Self-managing Kafka is operationally demanding. Managed options such as Confluent Cloud, AWS MSK, or Redpanda remove much of that overhead if you don't want to run the cluster yourself.
Do I actually need Kafka?
Kafka pays off when you have high-volume, real-time event streams or many services that need the same data. For simple background jobs, a lighter message queue is usually enough.
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