Change data capture (CDC) technology is the gateway to enabling real-time events on complex operational systems. This webinar will explore the requirements, use cases, and guiding principles for driving real-time operations with CDC. What Change Data Capture (CDC) does is it allows users to capture data in very small increments and not repeatedly copy unchanged data.(Transcribed)

Real-time processing on the analytics target does not generate real-time insights if the source data flowing into Kafka/Spark is hours or days old. And this is the logjam that change data capture technology (CDC) can break, especially for transactional data. CDC acquires live DB transactions and sends copies into the pipeline at near-zero latency, eliminating those slow and bloated batch jobs. It also reduces production processing overhead and cloud transfer bandwidth requirements.

Change data capture (CDC) identifies and captures just the most recent production data and metadata changes that the source has registered during a given time period, typically measured in seconds or minutes, and then enables replication software to copy those changes to a separate data repository. A variety of technical mechanisms enable CDC to minimize time and overhead in the manner most suited to the type of analytics or application it supports. CDC can accompany batch load replication to ensure that the target is and remains synchronized with the source upon load completion. Like batch loads, CDC helps replication software copy data from one source to one target, or one source to multiple targets. CDC also identifies and replicates changes to source schema (that is, data definition language [DDL]) changes, enabling targets to dynamically adapt to structural updates. This eliminates the risk that other data management and analytics processes become brittle and require time-consuming manual updates.