Change data capture (CDC) is an increasingly popular data pattern that converts database changes into events, enabling real-time data streaming for analytics and operational use cases without the need to replace existing relational databases. CDC can address the limitations of traditional databases by transforming them from static repositories to dynamic, reactive systems. It allows businesses to leverage event-driven architectures while maintaining their current infrastructure, offering solutions like reverse ETL and real-time data analysis. The implementation of CDC can vary, with techniques such as polling connectors, triggers, materialized views, and outbox tables being used to handle denormalization and event streaming. These methods help isolate the internal data model of a source system, minimizing dependencies and enhancing data utility. Tools like Debezium and platforms like Confluent Cloud support CDC, facilitating the development of data pipelines that integrate seamlessly into modern, real-time applications. As businesses increasingly rely on data-driven insights to inform their products, the role of CDC becomes more pivotal in bridging the gap between operational and analytical data needs.