Organizing a Data Pipeline from RabbitMQ to ClickHouse

This article explores the architecture of a data pipeline designed to stream user behavior events (clickstream) from RabbitMQ into the ClickHouse analytical database. The primary objective was to maintain an end-to-end latency of no more than five seconds. The author details a dual-storage strategy: saving raw JSON logs for audit and diagnostic purposes, alongside typed columns to facilitate efficient analytical queries. Significant attention is given to configuring RabbitMQ engine parameters, specifically rabbitmq_flush_interval_ms, and the necessity of monitoring latency metrics to ensure SLA compliance. This approach minimizes dependencies on external development teams and ensures high-performance, real-time data processing. The article serves as a practical guide for data engineers and architects working with high-load event collection and analysis systems.