As applications cover more aspects of our everyday life, it becomes increasingly difficult to provide users quick response times. Caching is used to tackle a wide variety of those problems, however, in many situations, applications require real-time data. On top of that we data to be aggregated, enriched, or otherwise transformed for further consumption or follow-up processing. In these use cases, Kafka comes in helpful.
Some of the main challenges that monolith applications face are having low availability and handling service disruptions. This is where the use of Apache Kafka for asynchronous communication between microservices can help you avoid bottlenecks that monolithic architectures with relational databases would likely run into.
I was tasked with a project that involved choosing between AWS Kinesis vs Kafka. The choice, as I found out, was not an easy one and had a lot of factors to be taken into consideration and the winner could surprise you. In this article I will help to choose between AWS Kinesis vs Kafka with a detailed features comparison and costs analysis.