Siddhesh Chavan
What is CDC and how it's implemented.

What is CDC and how does it work post it’s implemented.

By Siddhesh Chavan in SQL Server on Jun 11 2024
  • Sreenath Kappoor
    Jun, 2024 19

    Change Data Capture(CDC) - It records changes to the data in a relational database management system.

    It captures insert, update, and delete operations applied to database tables and makes these changes available in a structured and timely manner.

    To implement CDC,

    1. We need to enable CDC on database and table.
    2. We can store the details and access at any time
    3. Proper cleanup and maintenance also needed(size become big)

    Used in , Real-time Data Integration, Auditing and Compliance, Efficient Data Replication(without effecting transaction tables)

    • 1
  • Alpesh Maniya
    Jul, 2024 3

    **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.

    • 0


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