Stream Processing vs. Real-Time OLAP: Flink, ClickHouse & Pinot Compared

Stream processing (Flink) and real-time OLAP (ClickHouse, Pinot, Druid) are often confused because both market themselves as 'real-time analytics.' The key distinction is computation boundary: stream processors perform continuous, stateful precomputation on data in motion using event-time semantics and watermarks, while OLAP engines perform interactive, ad hoc query-time computation on stored columnar data. The recommended architecture uses Kafka as a durable backbone connecting both layers — Kafka → Flink → Kafka → OLAP — so Flink handles enrichment, deduplication, and windowing upstream, reducing the compute burden on the OLAP serving layer. Common pitfalls include connecting BI tools directly to stream processors and using OLAP engines for continuous stateful ETL. The post also covers streaming databases as a hybrid pattern, TCO trade-offs, and schema management best practices.