Change Data Capture (CDC) is a software design technique that enables the identification and tracking of changes in data across systems, facilitating efficient and real-time data transfer from source to target systems. Traditional batch data capture methods often suffer from inefficiencies and high latency, as they do not account for changes in data between intervals, whereas CDC improves performance by capturing changes continuously and in real-time, which is crucial for systems requiring up-to-date data insights. CDC is particularly beneficial for preventing dual-writes in microservices architectures, ensuring regulatory compliance, enabling real-time data streaming, and facilitating asynchronous data replication. Common CDC methods include Date Column Differences, Table Differences, Trigger-Based, and Log-Based, with the latter being favored for its reliability, scalability, and minimal impact on source systems, despite the complexities of dealing with various database log formats. Log-Based CDC is generally recommended for large-scale operations due to its ability to decouple source and target systems and its robust community support, making it an ideal solution for constructing accurate and actionable data pipelines.