apache flink

In a stateful streaming application the state of an application is one of the most important part. In Apache Flink we have the possibility to kind of backup the state with a so called savepointing mechanism. From these savepoints you as a developer or operations manager are able to stop-and-resume, fork, or update your Flink jobs (more to read about savepoints in the docs). So it’s a great possibility to try out different implementations of a fork for a Flink application....

Coming from Kafka-Streams continuous delivery (CD) is quite an easy task, and almost no effort has to be done compared to Apache Flink. Because the state of a Kafka-Streams application is stored in Kafka, and it can build up the state after a redeployment from so-called changelog topics, therefore Kafka-Streams is also bounded to have source and sink to Apache Kafka. Apache Flink on the other hand has the freedom to choose from a variety of source systems, e....