Real-Time Data Processing with Apache Flink and DBZM Connectors

Exploring the utilization of Apache Flink for processing real-time data streams, with a focus on Kafka topics and different APIs within Flink such as the data streaming API, table API, and Flink SQL. Discussing the functionality and importance of BZM as a change data capture tool for database transactions, and the application of the outbox pattern for processing orders and sending invoices efficiently. Highlighting the significance of unified change event formats, semantic data structures with DBZM, and considerations for production deployment with role-based access control and managed connectors.