Designing scalable, fault tolerant, and maintainable stream processing systems is not trivial. The Kafka Streams Java library paired with an Apache Kafka cluster simplifies the amount and complexity of the code you have to write for your stream processing system.

Unlike other stream processing systems, Kafka Streams frees you from having to worry about building and maintaining separate infrastructural dependencies alongside your Kafka clusters. However, you still need to worry about provisioning, orchestrating, and monitoring infrastructure for your Kafka Streams applications.

Heroku makes it easy for you to deploy, run, and scale your Kafka Streams applications by using supported...

Event-driven architectures are on the rise, in response to fast-moving data and constellations of inter-connected systems. In order to support this trend, last year we released Apache Kafka on Heroku - a gracefully integrated, fully managed, and carefully optimized element of Heroku's platform that is the culmination of years of experience of running many hundreds of Kafka clusters in production and contributing code to the Kafka ecosystem.

Today, we are excited to announce additional plans and pricing in our Kafka offering in order to make Apache Kafka more accessible, and to better support development, testing, and low volume production needs.

Many of the compelling and engaging application experiences we enjoy every day are powered by event-based systems; requesting a ride and watching its progress, communicating with a friend or large group in real time, or connecting our increasingly intelligent devices to our phones and each other. Behind the scenes, similar architectures let developers connect separate services into single systems, or process huge data streams to generate real-time insights. Together, these event-driven architectures and systems are quickly becoming a powerful complement to the relational database and app server models that have been at the core of Internet applications for over twenty years.

At Heroku, we...