I’ve been evaluating integration approaches for connecting our ERP system with Adobe Experience Cloud, and I’m torn between bulk data migration with scheduled syncs versus implementing real-time integration via APIs. We have about 2M customer records, 500K active opportunities, and high transaction volume.
Bulk migration seems simpler initially - extract, transform, load on a schedule. But I’m concerned about data latency and consistency issues. Real-time sync offers immediate data availability but adds complexity with API rate limits, error handling, and maintaining connection stability.
We need data consistency across systems, but our business processes might not require real-time updates for all data types. Some fields like customer demographics could sync daily, while order status might need real-time updates. Has anyone implemented a hybrid approach? What factors should drive this architectural decision? Looking for practical experiences with both approaches in enterprise environments.
We use database triggers for CDC in our ERP system, which publishes change events to Apache Kafka. AEC consumers pull from Kafka and update via REST API. For the dead letter queue, we used AWS SQS with exponential backoff retry logic. Failed messages after 5 retries go to a monitoring dashboard for manual review. This architecture handles about 50K updates daily with 99.9% success rate.
The CDC approach sounds promising. How did you implement change detection - database triggers, application-level tracking, or middleware solution? Also curious about the dead letter queue implementation - did you build custom or use existing tools?
One major consideration is error recovery. With bulk migration, you can easily retry failed batches and have clear success metrics. Real-time sync requires sophisticated error handling - what happens when an API call fails? Do you queue it? How long do you retry? We built a dead letter queue system for failed real-time updates, which added significant complexity. If your business can tolerate 15-30 minute delays, scheduled micro-batches might be the sweet spot.