What is CDC and how it's implemented.

**Change Data Capture (CDC)** is a method to track changes in a database so that actions can be taken based on those changes. It's essential for data consistency, synchronization, and real-time analytics. ### Implementation Methods: 1. **Database Triggers:** - **Pros:** Immediate change capture. - **Cons:** Can affect performance. 2. **Log-Based CDC:** - **Pros:** Minimal performance impact, scalable. - **Cons:** Complex setup. 3. **Timestamp-Based CDC:** - **Pros:** Simple implementation. - **Cons:** Requires schema changes, less efficient. 4. **Difference-Based CDC:** - **Pros:** No continuous monitoring. - **Cons:** Not real-time, higher storage needs. ### Use Case: Real-time Data Synchronization **Scenario:** A retail company synchronizes online and in-store inventory in real time. **Implementation:** 1. **Setup CDC Tool:** Install and configure a CDC tool (e.g., Debezium) to monitor sales and inventory tables. 2. **Data Streaming:** Stream captured changes to a message broker like Apache Kafka. 3. **Data Consumption:** A consumer service reads from Kafka and updates the other database. 4. **Monitoring:** Regularly monitor the CDC process for issues. ### Benefits: - **Real-time synchronization** ensures data consistency. - **Scalability** handles high transaction volumes. - **Minimal performance impact** with log-based methods.