Change Data Capture (CDC) is a technique used to track and capture changes made to data in a database. The primary goal of CDC is to identify and extract only the changed data from the source database, rather than extracting and processing the entire database each time. This approach reduces the amount of data transferred and processed, leading to improved efficiency and performance.

CDC monitors the database for any modifications, such as inserts, updates, and deletes, and captures the details of these changes in a structured format, typically stored in a separate data structure, called a change log or a change stream. This change log contains metadata about the changes, including the type of operation (insert, update, delete), the affected rows or records, and the timestamp of the change.

CDC plays a crucial role in enabling efficient data integration, synchronisation, and real-time processing, making it a valuable tool for data engineers working with large-scale data systems.

Ways to carry out CDC

Change Data Capture (CDC) can be done using different techniques, depending on the specific database technology and the system.

1. Database triggers: By creating triggers on the relevant tables in the transactional database, you can capture the changes and store them in a separate change log table or propagate them to another system or process.

2. Log-based CDC: Log-based CDC involves reading and analysing the transaction logs or redo logs of the database to capture changes. The transaction logs contain a record of every change made to the database, including the type of operation, the affected rows or records, and the transaction timestamp. This approach is often used when direct access to the transaction logs is available and supported by the DBMS.

3. Query-based CDC: Query-based CDC is a method of capturing and tracking changes made to a database by monitoring and querying the database directly. Unlike other CDC methods that rely on transaction logs or triggers, query-based CDC involves executing queries against the source database to identify and extract the changes.

4. CDC frameworks: Some database management systems provide built-in CDC frameworks or tools that simplify capturing and processing changes. These frameworks utilise underlying log or trigger mechanisms to identify and extract the changes efficiently.

5. Third-party CDC tools: Several third-party tools like Debezium, Attunity CDC, Oracle GoldenGate, and IBM InfoSphere Data Replication offer CDC capabilities for a wide range of database technologies, providing a more standardised approach to capturing and processing changes.

Advantages of log-based CDC

Log-based CDC offers several advantages compared to other CDC methods.

1. Real-time or near real-time data availability: Log-based CDC enables the capture and propagation of changes in real-time or near real-time. By analysing the transaction logs, the CDC process can capture changes as they occur, providing up-to-date data availability for downstream systems, analytics, reporting, and other applications that rely on fresh data.

2. Transactional consistency: Log-based CDC ensures transactional consistency when capturing changes. Since the logs contain a record of all transactions, the changes captured from the logs represent a consistent state of the data. This is particularly important when dealing with highly transactional databases that require maintaining data integrity across tables or complex relationships.

3. Efficient and incremental processing: By analysing the transaction logs, log-based CDC can extract only the new log entries since the last capture point. This incremental processing minimises the amount of data to be processed, reducing the resource consumption and improving the overall efficiency of the CDC process.

4. Low impact on source database: Log-based CDC operates externally to the source database, reading and analysing the transaction logs separately. This separation ensures minimal impact on the performance and resource utilisation. Unlike triggers or direct queries, log-based CDC does not introduce additional overhead, making it a suitable option for high-volume transactional systems.

5. Capturing a complete set of changes: Log-based CDC captures a comprehensive set of changes made to the database, including inserts, updates, and deletes. It covers all modifications at the transaction level, ensuring that no changes are missed or omitted during the capture process.

6. Support for schema changes: Log-based CDC can handle schema changes seamlessly. Since the transaction logs capture the before and after states of the data, the CDC process can adapt to schema modifications, such as table structure changes, column additions, or deletions. This flexibility allows for smoother handling of evolving data schemas.

7. Future growth: In principle, log-based CDC can be implemented across various database management systems (DBMS) as long as they provide transaction logs or redo logs. This database-agnostic capability makes the solution future ready, allowing for room to extend the support to DBMSs besides MySQL.

Thus, log-based CDC provides a powerful CDC solution for capturing and processing data changes, enabling real-time integration, analytics, reporting, and maintaining data consistency across systems.