We often see data platforms combining Kafka, Flink, and ClickHouse. Let's discuss when it works, when it's overkill, and where the boundary lies.

BigData Boutique blog

The KFC Architecture Blueprint combines Kafka, Flink, and ClickHouse into an end-to-end real-time data processing stack. Kafka handles event ingestion and decoupling, Flink manages stateful stream processing (windowed joins, sessionization, exactly-once semantics), and ClickHouse delivers sub-second analytical queries at scale. The pattern suits use cases like fraud detection, clickstream analytics, IoT telemetry, and financial market data. However, the full stack is often overkill — many workloads can skip Flink entirely by using ClickHouse's Kafka Table Engine with materialized views. The practical guidance: start with Kafka feeding directly into ClickHouse, and only add Flink when you genuinely need stateful joins, complex windowing, or exactly-once delivery with side effects.

The KFC Architecture Blueprint: Kafka, Flink, and ClickHouse