Problem
Operational analytics pipelines often need low-latency change propagation from transactional databases to event-driven consumers, but direct polling introduces delay and consistency issues.
Solution
Implemented a CDC-first architecture using Debezium + Kafka Connect for SQL Server change capture, with Kafka topics bridged to MQTT for low-latency downstream consumption and monitoring.
Architecture
The data path is:
SQL Server (CDC) -> Debezium Connector -> Kafka Topics -> MQTT Bridge -> Subscribers
Reliability Notes
- Connector-driven CDC removed application-level polling complexity.
- Topic-based buffering enabled decoupled consumers.
- Dockerized dependencies reduced setup variance across environments.