How I wish these operators had existed a few years ago when I was setting up Flink… https://github.com/GoogleCloudPlatform/flink-on-k8s-operator https://www.ververica.com/blog/google-cloud-platforms-flink-operator-for-kubernetes

This is a great watch for those beginning their journey with Flink.

The DA Platform is a huge step forward for running Flink at scale. I was lucky enough to see a demo and was really impressed. Far more advanced that the what can be achieved with Dataflow at the moment.

I’m currently bench-marking Flink against Google Cloud Dataflow using the same Apache Beam pipeline for quantitative analytics. One observation I’ve seen with Flink is the tail latency associated with some shards. Google Cloud Dataflow can optimise away stragglers in large jobs using “Dynamic Workload Rebalancing". As far as I know, Flink is currently unable to perform similar optimisations.

Apache Beam and Apache Flink are both distributed computing frameworks for processing large amounts of data in parallel, but they have some fundamental differences in their design and functionality. Apache Beam is a unified programming model for batch and streaming data processing, which provides a high-level API that allows developers to write data processing pipelines that can run on various execution engines, including Apache Flink, Apache Spark, and Google Cloud Dataflow.