Change Data Capture (CDC)

How Does Change Data Capture Work?

CDC detects changes in a source system and makes them available to downstream systems. The exact process depends on the database, capture method, and replication platform.

1. Identify the source: Select the database, table, or system whose changes need to be captured.
2. Detect changes: Track inserts, updates, and deletes using transaction logs, database features, timestamps, or other supported mechanisms.
3. Capture change records: Record the changed data along with relevant metadata, such as operation type and change position.
4. Deliver changes: Send captured changes to a target database, data warehouse, streaming platform, or data pipeline.
5. Apply updates: Process the incoming changes so the destination reflects the required state of the source data.

For example, an e-commerce database can use CDC to send new orders and updates to a data warehouse as transactions occur. Analytics systems can then consume these changes without repeatedly loading the entire orders table.

What Are the Types of Change Data Capture?

CDC implementations use different techniques to identify changes.

Log-Based CDC

Log-based CDC reads database transaction logs to identify inserts, updates, and deletes. It can capture changes with relatively low impact on application queries, although its behavior depends on database support, log retention, permissions, and connector configuration.

Trigger-Based CDC

Trigger-based CDC uses database triggers to record changes when data is inserted, updated, or deleted. It can provide detailed change records, but triggers may add overhead to write operations and require additional database objects.

Timestamp-Based CDC

Timestamp-based CDC checks columns such as updated_at to identify records that changed after a previous extraction. It is relatively straightforward to implement, but it may not reliably capture hard deletes and depends on accurate timestamp handling.

Snapshot-Based Change Detection

Snapshot-based methods compare successive copies or snapshots of data to identify differences. This can be useful when log-based or trigger-based capture is unavailable, but it may require more processing and data movement than capturing changes directly.

Why Is Change Data Capture Important?

CDC helps organizations keep data synchronized across systems while avoiding repeated full-table extracts when only a small portion of the data has changed.

• Incremental data loading: Transfer changed records rather than reprocessing the entire dataset.
• Near-real-time analytics: Make new and updated information available to analytical systems with low delay.
• Database replication: Propagate source changes to supported target systems.
• Reduced unnecessary processing: Limit repeated data extraction when an incremental approach is suitable.
• Event-driven workflows: Deliver changes to downstream applications that respond to database events.
• Data synchronization: Keep operational and analytical systems aligned as records change.

CDC requires careful handling of ordering, duplicate delivery, deletes, schema changes, and recovery after interruptions. Capturing changes does not automatically guarantee that every destination remains perfectly synchronized.

What Is the Difference Between CDC and Data Replication?

CDC and data replication are related but distinct concepts. CDC identifies and captures changes, while replication copies or applies data to another system. CDC is often used as the mechanism that powers ongoing replication.

Aspect	Change Data Capture (CDC)	Data Replication
Main purpose	Detect and capture data changes	Maintain copies of data across systems
Primary function	Identify inserts, updates, and deletes	Copy initial data and apply subsequent changes
Data movement	Supplies changes to downstream processes	Transfers data or changes to replicas
Typical use	Incremental pipelines and change-driven workflows	High availability, recovery, distributed access, and synchronization
Relationship	Can provide the change stream	Can use CDC to keep replicas updated

A replication solution may use CDC, but replication can also use other techniques, depending on the database and system architecture.

Related Data Concepts

• Data Replication
• Data Synchronization
• Data Pipeline
• Data Ingestion
• ETL
• ELT
• Database Transaction Log
• Real-Time Data Processing

Related Data Tools and Resources

Explore the best Data Integration Tools to connect databases and applications, Data Pipeline Tools to manage data movement, and ETL Tools to prepare data for analytics and reporting.

Frequently Asked Questions

What does CDC stand for in data?

CDC stands for Change Data Capture. It is a technique for identifying and capturing changes to data so they can be processed or delivered to other systems.

What changes can CDC capture?

Depending on the implementation, CDC can capture inserted records, updated records, and deleted records. The available operations and metadata depend on the source database and capture method.

Is CDC real-time?

CDC can support near-real-time or continuous change delivery, but actual latency depends on the database, capture mechanism, network, processing pipeline, and target system.

Is CDC the same as database replication?

No. CDC captures changes in a source system, while database replication maintains copies of data across systems. CDC is often used to propagate changes as part of a replication solution.

What tools support CDC?

CDC is supported by database-native features, managed cloud services, and data integration platforms. Examples include Debezium, which captures database changes using supported connectors, and database-specific replication or change-stream features.