Apache Flink on K8s and Kafka: PyFlink, Go, ops, and managed pricing

Apache Flink is a framework for stateful computations over unbounded and bounded data streams. Teams adopt it for correct, low-latency streaming with event-time semantics (watermarks), fault tolerance (checkpoints), controlled upgrades (savepoints), and operational surfaces (metrics and REST). This guide targets DevOps and Go/Python developers. It compares deployment models (self-managed vs managed), explains core architecture, covers Kubernetes (Helm and Operator) and standalone setups, contrasts Flink with Spark, Kafka Streams, Beam, and streaming databases, and shows PyFlink plus Go integration patterns including LLM and AI-oriented pipelines. For broader context on data infrastructure patterns including object storage, databases, and messaging, see Data Infrastructure for AI Systems: Object Storage, Databases, Search & AI Data Architecture. What is Apache Flink and why teams use it for real-time processing Apache Flink is explicitly positioned as a stateful stream processing en