Change Data Capture (CDC) Implementation
Automated change tracking and replication from source databases with minimal performance impact and data consistency guarantees.
Why This Matters
What It Is
Automated change tracking and replication from source databases with minimal performance impact and data consistency guarantees.
Current State vs Future State Comparison
Current State
(Traditional)Timestamp-based incremental extracts query source databases repeatedly checking last_modified_date columns. Full table scans on large tables cause performance degradation on source systems. Deleted records not captured (only inserts and updates). Batch-based extraction creates data staleness. High database load during extraction windows impacts transactional system performance.
Characteristics
- • Debezium
- • Oracle GoldenGate
- • AWS DMS
- • Informatica PowerCenter
- • Apache Kafka Connect
Pain Points
- ⚠ Schema evolution can break CDC pipelines.
- ⚠ High-volume environments may experience latency and throughput issues.
- ⚠ Ensuring data consistency during capture and delivery is challenging.
- ⚠ Tool compatibility issues with various databases and ERP systems.
- ⚠ Licensing costs for enterprise CDC tools can be high.
- ⚠ Operational overhead for ongoing monitoring and maintenance.
- ⚠ Complexity in integrating CDC into legacy ETL workflows.
Future State
(Agentic)AI-optimized CDC implementation reads database transaction logs (MySQL binlog, PostgreSQL WAL, SQL Server transaction log) to capture all data changes (inserts, updates, deletes) with minimal source system impact. Machine learning optimizes log reading patterns and checkpointing to balance data freshness with resource consumption. Automated schema evolution handling adapts to DDL changes without pipeline disruption. Exactly-once delivery guarantees ensure no data loss or duplication. Intelligent batching and compression minimize network bandwidth. Real-time monitoring of CDC lag with auto-scaling of capture processes. Supports heterogeneous databases with unified change event format.
Characteristics
- • Database transaction logs (binlog, WAL, etc.)
- • Database schema metadata
- • CDC checkpoint state
- • Target system acknowledgments
- • Historical CDC performance metrics
- • Database connection pools
Benefits
- ✓ Real-time data capture (seconds vs hours/days)
- ✓ 95-99% reduction in source system impact (<1-2% vs 10-30%)
- ✓ 100% change capture including deletes
- ✓ No source schema modification required
- ✓ Exactly-once delivery guarantees
Is This Right for You?
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across multiple industries
- • Higher complexity - requires more resources and planning
- • Moderate expected business value
- • Time to value: 2-6
- • (Score based on general applicability - set preferences for personalized matching)
You might benefit from Change Data Capture (CDC) Implementation if:
- You're experiencing: Schema evolution can break CDC pipelines.
- You're experiencing: High-volume environments may experience latency and throughput issues.
- You're experiencing: Ensuring data consistency during capture and delivery is challenging.
This may not be right for you if:
- High implementation complexity - ensure adequate technical resources
- Requires human oversight for critical decision points - not fully autonomous
Parent Capability
Data Integration & ETL
Modern data integration platform with real-time streaming, CDC, and AI-powered data mapping achieving significant reduction in integration development time.
What to Do Next
Related Functions
Metadata
- Function ID
- function-etl-change-data-capture