Data Engineering

When you already have systems tracking their own state in MongoDB, you can turn that into a real-time stream of structured events without rewriting application logic. This approach captures every meaningful change from Mongo, tags it with relevant metadata, and makes it instantly queryable in ClickHouse — all through a generic, reusable pattern. The idea: One fixed event envelope for all sources Dynamic tags/attributes defined in config files No code changes when onboarding new collections 1. The Fixed Event Envelope Every CDC message has the same top-level structure, no matter what source system or collection it came from: ...

Once your MongoDB change streams are flowing through Kafka and landing in ClickHouse, you’ve got a live, queryable event history for every state change in your systems. The obvious next step: start using it immediately — even before you build full-blown dashboards or machine learning models. 1. Detect Missing or Late Events One of the fastest wins is catching when something doesn’t happen. If you know a collection normally sees certain events every day, you can query for absences: ...

This guide shows how to wire MongoDB Atlas → Amazon MSK (Kafka) → ClickHouse on AWS so that change events from existing MongoDB apps are captured in a fast, queryable store. You’ll get: A lossless CDC stream of MongoDB changes into Kafka An optional config‑driven normalizer to add tags/attributes A ClickHouse sink for sub‑second queries and analytics Security and cost controls that work in a typical enterprise VPC 0) Architecture at a Glance MongoDB Atlas (Change Streams) │ (Atlas Kafka Source Connector) ▼ Amazon MSK (Kafka) ──► [Optional] Normalizer (Kafka consumer) │ │ │ └─ emits normalized events ▼ ClickHouse on AWS ◄─────────────┘ (Kafka Engine table or small consumer) Why this shape? Atlas produces change events; MSK is your durable bus; ClickHouse gives you fast, tag‑rich queries and easy rollups. ...