IntegrationAdvancedcdcchange-data-capturefiltering
Implement CDC high-volume event filtering to reduce subscriber noise
Real World Scenario
Change Data Capture on Account fires 400k events daily but warehouse only cares about billing address and credit tier changes, wasting Kafka throughput.
Expected Answer
• Enable CDC with field-level selection minimizing published change events
• Subscriber filters on ChangeEventHeader changedFields before processing
• Separate CDC entities for high-churn vs stable attributes if needed
• Middleware drop rules documented with business justification
• Periodic full reconcile catches missed business-relevant changes
• Monitor filtered vs processed ratio tuning field selection
• Avoid subscribing to formula field changes causing noise
Follow-Up Questions & Answers
Click to expand — each follow-up includes a direct, interview-ready answer
Main difference: use case and scale. Enable CDC with field-level selection minimizing published change events. Subscriber filters on ChangeEventHeader changedFields before processing. Pick based on your integration pattern and team capability. CDC without field strategy floods subscribers—architect publish boundary at Salesforce not only Kafka filter. Optimize for scale and operational observability.
Architect Perspective
CDC without field strategy floods subscribers—architect publish boundary at Salesforce not only Kafka filter.