Pacing messages are added to the Beam sport activity tracker by comparing short term speed metrics against their long term counterparts.

Late droppable elements are detected by a timer in a stateful DoFn and sent to a Beam side output instead of being discarded without notice.

A stateful DoFn with Beam state and timers fixes the gRPC batch size and maximum wait time instead of leaving the bundle size to the runner.

Batching gRPC calls in a stateless DoFn so one request covers a whole bundle, cutting the time a Beam Python pipeline spends on enrichment.

Data augmentation in Beam Python by calling a gRPC service once per input element, running on a local Flink cluster with Kafka as the source.

A sport activity tracker in Beam Python, built first with native transforms and then with Beam SQL, showing the limits of Beam SQL in the Python SDK.

Two Beam Python pipelines compute average word length from a Kafka topic, one emitting a global average and one using a sliding time window.

Set up a local Apache Flink and Kafka environment, then build two Beam Python streaming pipelines for top K frequent words and longest word length.

Deploy an Apache Beam Python pipeline to a Flink session cluster on minikube, packaged as a Docker image and submitted as a Kubernetes job.

Deploy a PyFlink application to minikube with the Flink Kubernetes Operator, alongside a Kafka cluster that provides its source and sink topics.