Glossary
What is Change Data Capture? CDC Fundamentals
Learn the fundamentals of Change Data Capture (CDC), a critical pattern for real-time data synchronization. Understand CDC patterns, implementation approaches, and how CDC integrates with modern data streaming architectures.
What is Change Data Capture? CDC Fundamentals
Change Data Capture (CDC) is a design pattern that identifies and captures changes made to data in a database, then delivers those changes in real-time or near real-time to downstream systems. Rather than periodically querying entire tables to detect changes, CDC monitors database transaction logs or triggers to capture inserts, updates, and deletes as they occur.
For data engineers and database administrators, CDC solves a fundamental challenge: how to keep multiple systems synchronized without impacting database performance or creating data inconsistencies.
Why Change Data Capture Matters
Traditional data synchronization approaches rely on batch processing—running queries at scheduled intervals to compare source and target data. This approach has significant limitations:
Performance Impact: Full table scans consume database resources and slow down operational systems, especially as data volumes grow.
Data Freshness: Batch intervals create latency between when changes occur and when downstream systems reflect those changes. For analytics, this means stale dashboards. For operational systems, this means outdated information driving business decisions.
Deleted Records: Standard queries cannot detect deleted records unless the database implements soft deletes with timestamp columns.
CDC addresses these issues by capturing changes at the source with minimal overhead, enabling real-time data pipelines and maintaining an accurate change history.
Core CDC Patterns
There are several approaches to implementing CDC, each with different trade-offs:
Log-Based CDC
Log-based CDC reads database transaction logs (also called write-ahead logs or WAL) to identify changes. This is the most efficient and comprehensive approach.
How it works: Databases write all changes to transaction logs before applying them to tables. CDC tools read these logs, parse the change events, and emit them to downstream consumers.
Advantages:
Minimal performance impact on source database
Captures all changes including deletes
No schema modifications required
Preserves exact order of operations
Limitations:
Requires appropriate database permissions
Log format varies by database system
Log retention policies must accommodate CDC processing
Trigger-Based CDC
Trigger-based CDC uses database triggers to capture changes. When a row is inserted, updated, or deleted, a trigger fires and writes change information to a separate table.
How it works: Triggers are created on source tables to execute custom logic whenever data changes occur. This logic typically inserts change records into a "shadow" or audit table that CDC consumers read.
Advantages:
Works with any database supporting triggers
Can include custom business logic
Change data explicitly stored and queryable
Limitations:
Performance overhead on every write operation
Requires schema modifications
Trigger maintenance complexity
Can be disabled by users with appropriate permissions
Query-Based CDC
Query-based CDC periodically queries tables for changes, typically using timestamp columns (e.g., updated_at, created_at).
How it works: A scheduled process queries tables using filters like WHERE updated_at > last_processed_time to identify modified records.
Advantages:
Simple to implement
No special database permissions
Works with any database
Limitations:
Cannot reliably detect deletes
Requires timestamp columns
Performance impact from repeated queries
Potential race conditions with concurrent updates
Not truly real-time
CDC in the Data Streaming Ecosystem
CDC has become a cornerstone of modern data architectures, particularly in streaming ecosystems built around Apache Kafka.
Debezium and Kafka Connect
Debezium is the most widely-used open-source CDC platform. It provides Kafka Connect source connectors that monitor databases and stream changes to Kafka topics.
This configuration creates a connector that reads PostgreSQL's write-ahead log and publishes change events to Kafka topics named prod-server.public.orders and prod-server.public.customers.
Event Structure
CDC events typically follow a standardized structure containing:
Before state: The row's values before the change (null for inserts)
After state: The row's values after the change (null for deletes)
Operation type: INSERT, UPDATE, or DELETE
Metadata: Timestamp, transaction ID, source position
CDC Architecture Pattern
A typical CDC streaming architecture looks like this:
This architecture enables multiple downstream systems to consume the same change stream independently, each processing events at their own pace.
Governance and Monitoring CDC Pipelines
As CDC pipelines become critical infrastructure, proper governance and observability become essential. Without visibility into data flows, organizations struggle to debug issues, ensure data quality, and maintain compliance.
Governance platforms provide unified visibility across CDC pipelines built on Kafka, enabling teams to:
Monitor CDC connector health and lag metrics
Track data lineage from source databases through Kafka topics to downstream consumers
Enforce data governance policies on change event streams
Troubleshoot data quality issues by inspecting change events
Control access to sensitive change data with topic-level permissions
This visibility is particularly valuable when managing multiple CDC sources feeding a data platform, where understanding data provenance and ensuring consistency across replicated systems requires centralized monitoring.
Common CDC Use Cases
Database Replication
Replicate production databases to analytics environments without impacting operational performance. CDC enables near real-time replication while avoiding expensive database read replicas.
Cache Invalidation
Keep caches synchronized with source-of-truth databases. When data changes, CDC events trigger cache updates or invalidations, preventing stale cache data.
Search Index Synchronization
Maintain search indexes (Elasticsearch, Solr) in sync with relational databases. CDC events update indexes immediately when source data changes.
Data Warehouse Loading
Replace batch ETL processes with continuous data ingestion. CDC streams feed data warehouses with incremental updates, reducing latency and improving data freshness.
Event-Driven Architectures
CDC converts database changes into events that trigger business processes. For example, a new order insertion triggers inventory checks, payment processing, and shipping workflows.
Audit and Compliance
Maintain complete change history for compliance and auditing. CDC captures who changed what and when, creating an immutable audit trail.
Implementation Considerations
When implementing CDC, consider these factors:
Database Compatibility: Not all databases expose transaction logs equally. PostgreSQL and MySQL have mature CDC support, while some proprietary databases require vendor-specific tools.
Network and Latency: CDC introduces network hops between source databases and consumers. Plan for appropriate network capacity and monitor latency.
Schema Evolution: Database schema changes must be handled gracefully. Most CDC tools support schema evolution, but downstream consumers must adapt to schema changes.
Exactly-Once Semantics: Depending on failure scenarios, CDC might deliver duplicate events. Downstream systems should implement idempotent processing or deduplication.
Resource Planning: While CDC is efficient, reading transaction logs and publishing events consumes resources. Size connector infrastructure appropriately.
Summary
Change Data Capture is a foundational pattern for modern data architectures, enabling real-time data synchronization without the performance penalties and limitations of traditional batch approaches. Log-based CDC, implemented through tools like Debezium, provides the most robust solution by capturing all database changes with minimal source impact.
By integrating CDC with streaming platforms like Kafka, organizations build event-driven architectures that keep multiple systems synchronized in real-time. This capability powers use cases from data warehouse loading to microservices communication.
Success with CDC requires attention to monitoring, governance, and operational concerns. As CDC pipelines become critical infrastructure, comprehensive observability and governance tooling becomes essential for maintaining reliable data platforms.
Sources and References
Martin Kleppmann - "Designing Data-Intensive Applications" - Chapter on Stream Processing and CDC