Change Data Capture (CDC)

Change Data Capture is a method used to track and record changes made to a database table, allowing for real-time data updates to other systems.

On a high level, the CDC tracks changes in a source dataset, such as a transactional database. It automatically transfers those changes to a target dataset, like a data warehouse or analytics platform.

📋 CDC Use cases

• Replicate data in other target databases, such as Data Warehouses or Data Lakes.
• Stream processing based on data changes.
• Enable real-time fraud detection in financial systems by analyzing transactions as they occur.
• Optimize supply chain management by keeping inventory updates synchronized across locations.
• Support real-time analytics for customer behavior tracking or operational dashboards.
• Invalidate or update the cache.
• Async jobs based on data changes.

🗂️ CDC Types

While the overall concept of CDC is consistent, multiple approaches to implementing it are driven by varying use cases and system requirements. The most commonly used methods are:

📜 Log-based CDC

• The transactional database logs all changes into special files known as transaction logs.
• Tools like Debezium or AWS Database Migration Service handle log-based CDC by capturing and streaming changes from these logs to target systems.
• These logs are processed to publish changes to target systems using messaging queues or similar mechanisms, ensuring real-time or near-real-time synchronization.

⏯️ Trigger-Based CDC

• It uses stored procedures automatically executed when specific events occur on a table, such as row inserts, updates, or deletions.
• It captures data changes in a shadow table or directly publishes them in a message queue.

However, this approach can introduce performance overhead due to the frequent execution of triggers, which may impact the source database's efficiency under high transaction volumes.

⏱️ Timestamp-Based CDC

• Adds a special column to the table (e.g., last_modified) to reflect the most recent change.
• Compared to log-based CDC, timestamp-based methods may be less efficient in scenarios with frequent updates, as querying and comparing timestamps can result in higher processing overhead.
• Queries this field to fetch records updated since the last execution time.
• This method might be less reliable in distributed systems due to clock synchronization issues, where differences in system clocks across servers can result in inaccurate or missed updates.

⚖️ Log-based vs Trigger-Based vs Timestamp-Based

When selecting a CDC approach, consider the following factors:

• High data volumes may favor log-based CDC for efficiency, whereas smaller datasets might work well with timestamp-based or trigger-based CDC.
• Log-based CDC is typically the most suitable if near real-time updates are critical.
• Assess whether the source system supports transaction logs or triggers, which may limit your choices.
• Some methods, like trigger-based CDC, might require more configuration and maintenance.
• Consider how well the CDC method integrates with existing data pipelines, message queues, or analytics platforms.

📊 Comparison table:

✅ Advantages of CDC

1. Enables near real-time data synchronization. For example, a financial institution can use CDC to instantly update account balances across multiple systems when a transaction occurs, ensuring accurate and up-to-date information for customers and internal processes.
2. Reduces the need for full table scans, improving performance.
3. Facilitates event-driven architectures by integrating with message queues.
4. Supports secure data transfer through encryption and access controls, ensuring compliance with regulations like GDPR or HIPAA.

⚠️ Challenges of CDC

1. Some implementations can be complex and require careful configuration. Mitigation involves using standardized tools and thorough documentation.
2. This may introduce additional overhead on source systems. This can be addressed by optimizing database queries and scheduling CDC tasks during low-traffic periods.
3. Achieving true real-time updates can depend on the infrastructure and implementation. Using high-performance infrastructure and asynchronous messaging can help reduce delays.
4. Handling schema evolution in source systems can be challenging, especially when changes like column renaming or datatype modifications occur. Advanced CDC tools can help track and adapt to these changes dynamically.
5. In microservices architectures, maintaining consistency across loosely coupled systems using CDC requires careful orchestration and infrastructure planning to avoid data conflicts or duplication.

Production-ready CDC system

To build a production-ready CDC system, several critical considerations must be addressed:

1. Preserve Change Order: Changes must be delivered in the order they occurred to prevent inconsistent states in downstream systems.
2. Message Delivery Guarantees: The CDC system must support delivery guarantees, such as at least once and exactly once semantics. Missing a change event can result in an inconsistent system state.
3. Message Format Conversion: The system should support easy message conversion to accommodate different data formats expected by downstream systems.

To address these requirements, a pub/sub architecture with a message bus is highly recommended. When a change occurs in the source system, it pushes the change event to the message bus. The target system subscribes to the message bus and consumes changes as they arrive, ensuring reliable and consistent data transfer.