Stream & Unify Data Schemas with CDC

Imagine deploying schema changes with confidence—knowing your pipeline will handle them gracefully, consumers will stay healthy, and your data will stay consistent. That's the difference between hoping your CDC pipeline works and knowing it will. In this course you will learn how to build a working, vendor‑neutral CDC pipeline and a single, unified table from evolving source schemas. Starting with Debezium streaming changes from Postgres/MySQL into Kafka, you will use Schema Registry to enforce compatibility, then apply streaming SQL in Flink (or ksqlDB) to map, cast, and merge divergent fields into a canonical model. Finally, you will persist results to an Apache Iceberg table and query it instantly with Trino. Along the way, you’ll learn practical strategies to manage schema drift, choose compatibility modes (backward/full), and avoid breaking downstream consumers. Everything runs locally with Docker so you can reproduce it anywhere and take the same patterns to your cloud stack later. This course is designed for engineers working with Kafka, Debezium, and streaming SQL who need reliable schema evolution and canonical modeling skills. Learners should be familiar with Basic SQL, Docker, and familiarity with Kafka or streaming concepts. By the end of the course,you will be able to implement a small end‑to‑end CDC pipeline that streams from a source DB and unifies evolving schemas into a single queryable table

• Explain CDC fundamentals and schema evolution strategies
• Configure a local Schema Registry pipeline using Debezium and Kafka
• Use streaming SQL to unify divergent schemas into a canonical model

• Basic familiarity with the topic and terminology
• Readiness to practice through applied exercises or case work