Designing Scalable Streaming Data Pipelines with Apache Kafka Schema Enforcement, Real-Time Cleansing, and Event-Driven RAG Patterns

Author(s)	Saurabh Atri
Country	United States
Abstract	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.
Keywords	Apache Kafka, Schema Registry, Avro, Protobuf, Kafka Streams, Apache Flink, Data Quality, Streaming ETL, RAG, Vector Index, Event-Driven Architectures
Field	Engineering
Published In	Volume 16, Issue 2, July-December 2025
Published On	2025-09-17
Cite This	Designing Scalable Streaming Data Pipelines with Apache Kafka Schema Enforcement, Real-Time Cleansing, and Event-Driven RAG Patterns - Saurabh Atri - IJAIDR Volume 16, Issue 2, July-December 2025. DOI 10.71097/IJAIDR.v16.i2.1581
DOI	https://doi.org/10.71097/IJAIDR.v16.i2.1581
Short DOI	https://doi.org/g9626x

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