Understanding change data capture

Change data capture (CDC) has become a foundational technique for modern data pipelines. As organizations demand fresher data and more responsive analytics, CDC provides a mechanism to synchronize changes from source systems without repeatedly scanning entire datasets. For data engineering leaders building scalable, cost-effective pipelines, understanding CDC's capabilities and constraints is essential.

What is change data capture?

Change data capture is a pattern for identifying and capturing changes made to data in a source system, then propagating those changes downstream. Rather than extracting full datasets on a schedule, CDC tracks inserts, updates, and deletes as they occur, enabling near-real-time data synchronization.

CDC operates by monitoring the transaction log or change stream of a database. When a record is created, modified, or removed, CDC detects that event and makes it available for downstream processing. This approach minimizes the load on source systems and reduces the latency between when data changes and when those changes appear in analytics environments.

Two primary CDC approaches exist: log-based and trigger-based. Log-based CDC reads directly from a database's transaction log, offering low latency and minimal performance impact on the source system. Trigger-based CDC uses database triggers or application-level hooks to emit change events when data modifications occur. This approach works when direct log access isn't available, though it typically introduces more overhead on the source system.

Why CDC matters

The value of CDC becomes clear when considering the limitations of traditional batch extraction. Batch processes typically run on fixed schedules (hourly, daily, or weekly) and extract entire tables or large subsets of data each time. This approach works for many use cases, but it introduces latency, wastes compute resources on unchanged data, and can strain source systems during extraction windows.

CDC addresses these limitations by capturing only what has changed. This efficiency translates into several concrete benefits. Data freshness improves dramatically, with changes appearing in the warehouse within minutes rather than hours. Compute costs decrease because pipelines process smaller volumes of data. Source system performance remains stable because CDC avoids the heavy scans that batch extraction requires.

For use cases where timing matters (fraud detection, personalization engines, operational dashboards), CDC provides the data freshness that batch processes cannot. When a customer updates their address or a transaction is flagged as suspicious, CDC ensures that change flows downstream quickly enough to inform decisions while they still matter.

Key components of a CDC pipeline

A complete CDC implementation involves several components working together. At the source, a CDC tool monitors the database transaction log or change stream. This monitoring happens continuously, capturing events as they occur. The CDC tool then serializes these events into a format suitable for transmission, often JSON or Avro, and publishes them to a message queue or directly to the destination.

The destination (typically a cloud data warehouse or lakehouse) receives these change events and applies them to target tables. This application process requires careful handling. Events must be processed in order to maintain consistency. Duplicate events must be detected and handled appropriately. Schema changes in the source system must be managed without breaking the pipeline.

Between capture and application, transformation logic often sits. Raw change events contain metadata about the operation type (insert, update, delete) and the before and after values of affected columns. Transforming these events into analytics-ready tables requires logic to handle updates, merge changes, and maintain historical state when needed.

dbt plays a central role in this transformation layer. Using incremental models, dbt can process CDC events efficiently, applying only new changes rather than rebuilding entire tables. This approach maintains the performance benefits of CDC throughout the pipeline. dbt's testing framework ensures that transformed data meets quality standards, catching issues before they propagate to dashboards or downstream systems.

Common use cases

CDC excels in scenarios where data freshness directly impacts business outcomes. In e-commerce, inventory levels must reflect recent purchases to prevent overselling. CDC pipelines synchronize inventory changes from transactional databases to analytics platforms, enabling real-time visibility into stock levels across channels.

Financial services firms use CDC to monitor transactions for fraud patterns. When a suspicious transaction occurs, CDC ensures that fraud detection models see that transaction within seconds, not hours. This speed can mean the difference between blocking a fraudulent charge and allowing it to process.

Operational reporting benefits from CDC when business users need current data to make decisions. A logistics company tracking shipments needs to know where packages are now, not where they were at the end of yesterday's batch run. CDC pipelines keep operational dashboards current without the cost and complexity of querying production databases directly.

Customer data platforms rely on CDC to maintain unified customer profiles. When a customer updates their preferences in one system, CDC propagates that change across the organization's data infrastructure, ensuring consistent personalization and communication.

Challenges and considerations

CDC introduces complexity that teams must manage carefully. Schema evolution presents a persistent challenge. When a source system adds, removes, or renames a column, CDC pipelines must handle that change without breaking. Some CDC tools detect schema changes automatically and adjust downstream tables accordingly. Others require manual intervention. Either way, teams need processes to test and deploy schema changes safely.

Exactly-once delivery semantics require attention. In distributed systems, messages can be delivered multiple times due to network issues or system failures. CDC pipelines must detect and handle duplicate events to prevent double-counting or incorrect aggregations. Most modern CDC tools provide mechanisms for deduplication, but the transformation logic must implement these mechanisms correctly.

Backfilling historical data poses another challenge. When first implementing CDC, teams often need to load existing data before switching to change-based synchronization. This initial load must complete successfully and transition smoothly to CDC mode without gaps or duplicates.

Performance tuning becomes necessary as data volumes grow. CDC pipelines must keep pace with the rate of changes in source systems. If changes accumulate faster than the pipeline can process them, lag increases and data freshness degrades. Monitoring lag and adjusting pipeline capacity accordingly is an ongoing operational requirement.

Data contracts help manage some of these challenges by establishing explicit agreements between data producers and consumers about schema, freshness, and reliability. When a source system plans to make breaking changes, data contracts provide a framework for communicating those changes and coordinating updates across dependent systems.

Best practices

Start by identifying which tables truly need CDC. Not every table benefits from near-real-time synchronization. Focus CDC efforts on high-value, frequently changing data where freshness matters. Use batch extraction for slowly changing or less critical tables.

Design for idempotency from the start. Transformation logic should produce the same result whether a change event is processed once or multiple times. This property simplifies error handling and recovery. When a pipeline fails and needs to reprocess events, idempotent transformations prevent data corruption.

Implement comprehensive monitoring. Track lag between when changes occur in source systems and when they appear in the warehouse. Monitor error rates and pipeline throughput. Set alerts for when lag exceeds acceptable thresholds or when error rates spike. These metrics provide early warning of issues before they impact downstream users.

Use incremental models in dbt to process CDC events efficiently. Rather than rebuilding entire tables, incremental models apply only new changes. This approach maintains the performance benefits of CDC throughout the transformation layer. Configure incremental models with appropriate strategies: merge for updates and deletes, append for insert-only streams.

Test CDC pipelines thoroughly before deploying to production. Verify that the pipeline handles all operation types correctly: inserts, updates, deletes. Test schema change scenarios to ensure the pipeline adapts without breaking. Validate that deduplication logic works as expected. Use dbt's testing framework to assert data quality expectations on transformed tables.

Document the CDC implementation clearly. Future team members need to understand which tables use CDC, how change events are structured, and what transformation logic applies. This documentation reduces the time required to troubleshoot issues or extend the pipeline to new tables.

Plan for schema evolution. Establish processes for testing and deploying schema changes. When possible, make additive changes that don't break existing queries. When breaking changes are necessary, use model versioning to provide a migration path for downstream consumers.

The path forward

Change data capture represents a significant evolution in how organizations move and synchronize data. By capturing changes as they occur rather than repeatedly scanning entire datasets, CDC enables fresher data, lower costs, and more responsive analytics. The technique introduces complexity, but modern tools and frameworks have made CDC accessible to teams beyond the largest technology companies.

For data engineering leaders, CDC is a tool to deploy strategically. Not every pipeline needs near-real-time synchronization, but for use cases where freshness matters, CDC provides capabilities that batch processes cannot match. Combined with modern transformation frameworks like dbt, CDC pipelines can deliver reliable, tested, and well-documented data products that meet the demands of today's data-driven organizations.

Frequently asked questions

What is change data capture (CDC)?

Change data capture is a pattern for identifying and capturing changes made to data in a source system, then propagating those changes downstream. Rather than extracting full datasets on a schedule, CDC tracks inserts, updates, and deletes as they occur, enabling near-real-time data synchronization by monitoring the transaction log or change stream of a database.

Why do you need CDC?

CDC addresses the limitations of traditional batch extraction by capturing only what has changed, providing several key benefits. Data freshness improves dramatically with changes appearing in the warehouse within minutes rather than hours. Compute costs decrease because pipelines process smaller volumes of data, and source system performance remains stable by avoiding heavy scans that batch extraction requires. For use cases like fraud detection, personalization engines, and operational dashboards where timing matters, CDC provides the data freshness that batch processes cannot deliver.

What challenges arise when using transaction logs for change data capture?

CDC introduces several operational challenges that teams must manage carefully. Schema evolution presents a persistent challenge when source systems add, remove, or rename columns, requiring pipelines to handle changes without breaking. Exactly-once delivery semantics require attention to detect and handle duplicate events in distributed systems. Performance tuning becomes necessary as data volumes grow to ensure pipelines keep pace with source system changes, and backfilling historical data when first implementing CDC requires careful coordination to transition smoothly without gaps or duplicates.