Change data capture (CDC) is a technique used to identify and capture changes made to data in a database. It helps keep track of modifications and synchronize data across systems.
Some data integration methods that support real-time or near-real-time data processing include Change Data Capture (CDC), Data Replication, Message Queues, and Event Streaming. These methods allow for the continuous and timely transfer of data between systems to support real-time analytics and decision-making.
Tracking data changes across systems involves implementing data synchronization mechanisms, utilizing tools like change data capture (CDC), log-based replication, and event streaming. By capturing data changes in real-time and propagating them to different systems, organizations can ensure data consistency and integrity.
Data integration techniques are crucial in Big Data projects for combining and consolidating diverse data sources to provide a unified view. The commonly used techniques in Big Data projects include Extract, Transform, Load (ETL) processes, Change Data Capture (CDC), and data virtualization. ETL processes involve extracting data from multiple sources, transforming it to match the target system requirements, and loading it into a data warehouse or data lake. CDC techniques capture and replicate data changes in real time to keep the data synchronized across systems. Data virtualization enables access to data stored in different systems without physically moving or replicating it.
