Modern data products depend on low-latency, trustworthy streams that can evolve without breaking downstream applications. This article presents a practical blueprint for building scalable streaming data pipelines on Apache Kafka 1. We focus on three pillars: (1) schema enforcement using a central registry and compatibility policies 2-4; (2) real-time cleansing and enrichment with stateless and stateful operators on Kafka Streams or Apache Flink 5,6; and (3) event-driven Retrieval-Augmented Generation (RAG) patterns where model inference is triggered by events and grounded in fresh, streamed context 11. We provide reference architecture, configuration examples, correctness and cost metrics, and operational playbooks to reach predictable performance.
Saurabh Atri - (Wed,) studied this question.