Change Data Capture (CDC) Guide
Complete CDC implementation with log-based change tracking, sub-second latency, and 99.99% accuracy. Real-time data synchronization in 1-2 weeks.
CDC Use Cases
Real-Time Analytics
- Stream operational data to analytics warehouse
- Real-time dashboards and reporting
- Live business intelligence
- Event-driven analytics
Database Replication
- Active-active multi-region databases
- Disaster recovery and failover
- Read replicas for scaling
- Cross-database synchronization
Event-Driven Architecture
- Microservices data synchronization
- Event streaming to Kafka/Kinesis
- Real-time notifications and alerts
- Workflow automation triggers
Zero-Downtime Migration
- Continuous sync during migration
- Parallel run validation
- Instant rollback capability
- Cloud migration with no interruption
4-Phase CDC Implementation
Source Analysis & Planning
Analyze source database and design optimal CDC architecture for your requirements.
- Database version and CDC capability assessment
- Change volume and pattern analysis
- CDC method selection (log-based, trigger, query)
- Target system and transformation requirements
CDC Configuration & Initial Load
Configure CDC agents and perform initial data snapshot with minimal production impact.
- Enable transaction log reading (for log-based CDC)
- Configure change tracking tables and metadata
- Parallel initial snapshot with consistent point-in-time
- Transformation rules and filtering setup
Continuous Sync & Monitoring
Start continuous change capture with real-time monitoring and automated validation.
- Real-time change detection and capture
- Automated data validation and reconciliation
- Latency monitoring and alerting
- Error handling and automatic retry
Optimization & Scaling
Optimize performance and scale CDC infrastructure for production workloads.
- Throughput optimization and batching tuning
- Network compression and bandwidth optimization
- Horizontal scaling for high-volume changes
- Schema change handling and evolution
CDC Methods Comparison
| Factor | Log-Based CDC | Trigger-Based CDC | Query-Based CDC |
|---|---|---|---|
| Performance Impact | Minimal (<1%) | Moderate (5-10%) | High (10-20%) |
| Latency | Sub-second | 1-5 seconds | Minutes |
| Accuracy | 99.99% (all changes) | 99.9% (DML only) | 95-98% (polling gaps) |
| Setup Complexity | Low (automated) | Medium (trigger creation) | Low (query setup) |
| Database Support | Most modern databases | All databases | All databases |
| Schema Changes | Auto-detected | Manual trigger updates | Manual query updates |
| Cost | Low (minimal resources) | Medium (trigger overhead) | High (query overhead) |
| Best For | Production systems | Legacy databases | Low-volume tables |
AI-Powered vs Manual CDC Implementation
See how DataMigration.AI automates CDC implementation compared to traditional manual approaches
| Feature | DataMigration.AI | Manual CDC Setup |
|---|---|---|
| Implementation Time | 1-2 weeks (automated) | 4-8 weeks (manual) |
| CDC Method Selection | Automatic based on database analysis | Manual evaluation and decision |
| Configuration | Automated setup and tuning | Manual configuration |
| Latency | Sub-second (optimized) | 1-5 seconds (unoptimized) |
| Accuracy | 99.99% (all changes captured) | 95-98% (polling gaps) |
| Performance Impact | <1% (AI-optimized) | 5-10% (unoptimized) |
| Schema Change Handling | Automatic detection and adaptation | Manual updates required |
| Monitoring | Real-time AI-powered alerts | Basic monitoring |
| Cost (per month) | $2K-$5K | $10K-$20K (engineering time) |
| Expertise Required | Minimal (AI-guided) | Senior database engineer |
People Also Ask
What is change data capture (CDC)?
Change data capture (CDC) is a technology that identifies and captures changes (INSERT, UPDATE, DELETE) made to database tables in real-time. Log-based CDC reads transaction logs without touching production tables, achieving sub-second latency with minimal performance impact. CDC enables real-time analytics, database replication, event-driven architectures, and zero-downtime migrations.
How does log-based CDC work?
Log-based CDC reads database transaction logs (WAL in PostgreSQL, redo logs in Oracle, transaction log in SQL Server) to capture all data changes without querying production tables. AI agents parse log entries, extract change events, apply transformations, and stream to target systems with sub-second latency. This approach has minimal performance impact (<1%) and captures 100% of changes including schema modifications.
What databases support CDC?
Most modern databases support log-based CDC: PostgreSQL (logical replication), MySQL (binlog), SQL Server (CDC feature), Oracle (GoldenGate/LogMiner), MongoDB (change streams), Cassandra (CDC tables). Legacy databases without native CDC support can use trigger-based or query-based CDC. AI-powered CDC works across all database types with automated configuration and optimization.
How long does CDC implementation take?
AI-powered CDC implementation completes in 1-2 weeks including source analysis, CDC configuration, initial snapshot, continuous sync setup, and monitoring. Initial data load time varies by volume (1TB in 4-8 hours). Once operational, CDC runs continuously with sub-second latency. Manual CDC implementation takes 4-8 weeks for the same scope due to custom development and testing.
What is the performance impact of CDC?
Log-based CDC has minimal performance impact (<1% CPU/memory overhead) as it reads transaction logs asynchronously without touching production tables or adding triggers. Network bandwidth usage depends on change volume but typically 10-50 Mbps for high-transaction systems. AI optimization reduces impact through intelligent batching, compression, and adaptive throttling during peak loads.
Ready to Implement CDC?
Schedule a free CDC assessment to discover how AI-powered change data capture can achieve sub-second latency in 1-2 weeks.