Apache Kafka

We used it for event logging. It was used for application log collection. Was used with exception tracking and with core microservices of the web application. It helped us reduce cost and simplified operational monitoring.

We use Apache Kafka as message broker between our two client facing applications. We used ActiveMQ before but it had shortfalls of high availability and clustering. Kafka solved it on both fronts and gives a good business continuity.

Apache Kafka is used by our company as the "next generation" of messaging/data-streaming pipeline solutions, to replace our old legacy JMS-based messaging solution and enable the modern streaming API based applications. When it is used for messaging purposes, we shift the responsibility of data replay from the message source (publisher application) to the message destination (consumer application). This flexibility resolved the legacy issue of sources replaying the messages but impacting all subscribers to the same topic. When Kafka is used as the streaming pipeline, it is integrated seamlessly with the Spark/Spring Stream-based analytic solutions, as it is also a kind of distributed storage.

Apache Kafka is the most powerful and scalable streaming framework on the market. We have used Apache Kafka as a part of many real-time analytics solutions. It has a great performance [and is] easy to integrate with big data technologies like Spark. Due to its distributed nature, Apache Kafka is capable of operating very quickly and can handle millions of messages every second.

Kafka is being used for sending log information in real time and there[fore] can monitor apps and send these events to feed other apps. It's the core for send[ing] and receiv[ing] messages due to quantity of messages per second. Helps us to scale and manage the common errors in this type of problem.