We’re planning to integrate Adobe Experience Cloud’s event management module with our real-time attendance tracking system for a series of conferences and webinars. The goal is to sync attendee check-ins, session participation, and engagement metrics back to AEC in near real-time.
I’m evaluating two approaches: webhook-based push notifications versus polling the attendance API every few minutes. The webhook approach seems more efficient, but I’m concerned about handling webhook failures and ensuring data reconciliation if messages are lost. With polling, we have more control over the sync timing, but there’s inherent latency.
Our events typically have 200-500 attendees, with check-ins happening in bursts (everyone arriving within 30 minutes). We need attendance data accurate within 5-10 minutes for real-time reporting dashboards. What are the trade-offs between these approaches, and how do others handle latency and data consistency in similar integrations?
Burst check-in patterns (200–500 attendees in a 30-minute window) are exactly the scenario where webhook reliability gaps become visible and polling interval tuning becomes critical.
Diagnostic steps to size the decision correctly:
- Baseline your attendance platform’s webhook emit rate under burst load — instrument the source system to measure p95 emit latency during simulated concurrent check-ins before committing to either architecture.
- Audit Adobe Experience Platform’s Streaming Ingestion API rate limits in your licensed tier; confirm whether your org is on the 1 500 events/second or higher entitlement (verify in your version/contract).
- Measure your current polling endpoint’s response time under load — if the attendance API takes >30 seconds to respond during peak, a 2-minute polling interval effectively becomes 4+ minutes of lag.
- Identify whether your reporting dashboard consumes from AEP Real-Time Customer Profile or a downstream Customer Journey Analytics dataset — the acceptable latency ceiling differs materially between those two.
- Check for existing Adobe I/O Events or Adobe I/O Runtime entitlements in your org; these change the webhook failure-handling calculus significantly.
Recommended architecture for your load profile:
Given your 5–10 minute SLA and burst pattern, a hybrid approach outperforms either pure strategy:
- Primary path: webhooks into Adobe I/O Runtime (or a lightweight queue like Amazon SQS / Azure Service Bus in front of AEP Streaming Ingestion) to absorb burst spikes and provide retry buffering.
- Reconciliation path: a polling sweep every 8 minutes using the attendance API, diffing against already-ingested records via a checksum or
lastModified timestamp field.
Tuning parameters:
| Parameter |
Recommended Value |
| Webhook retry attempts |
3, with exponential backoff starting at 5s |
| Polling interval |
8 minutes (within your 10-minute SLA with headroom) |
| Queue visibility timeout |
90 seconds (must exceed your AEP ingestion acknowledgment window) |
| Reconciliation lookback window |
15 minutes to catch delayed emits |
| AEP Streaming batch size |
20–50 events per HTTP call to reduce connection overhead |
Monitoring/verification check:
Create an AEP Monitoring dashboard alert on ingestion failure rate exceeding 0.5% and cross-reference the row count in your attendance source against the Profile count in the target dataset segment after each event closes. A >2% discrepancy triggers a full reconciliation replay from the polling path. Verify dashboard refresh cadence in Customer Journey Analytics connection settings matches your ingestion SLA (verify in your version).
This draft is based on general Adobe Experience Cloud knowledge. It has not been verified against your specific version and environment. Practitioners: verify the steps and share your experience below.
We use webhooks for our event integrations and they work well once you implement proper error handling. The key is to have a retry mechanism with exponential backoff and a dead letter queue for failed deliveries. For data reconciliation, we run a nightly batch job that compares webhook-delivered records against the source system to catch any missed updates.
I’d recommend a hybrid approach. Use webhooks as the primary mechanism for immediate updates, but also implement a polling-based reconciliation process that runs every 15-30 minutes as a safety net. This gives you the best of both worlds - low latency from webhooks and data consistency from polling. The polling job only syncs records that have a timestamp newer than your last successful webhook, so it’s not doing redundant work under normal conditions.
The hybrid approach sounds promising. How do you handle the burst traffic scenario? During event check-in, we might receive 200 webhook notifications within 10 minutes. Does AEC’s webhook endpoint handle that volume reliably, or should we implement rate limiting on our side?
AEC 2022 has webhook rate limits of about 100 requests per minute per endpoint. For your burst scenario, you’ll definitely hit that limit. I’d suggest implementing a queue on your side - collect the check-in events in a message queue, then process them in batches to AEC at a controlled rate. You can batch up to 50 records per API call, which helps with throughput. This also gives you a natural place to implement retry logic and monitoring.
For latency handling, consider that webhook delivery itself can be delayed during network issues or if AEC’s receiving endpoint is under load. We’ve seen webhook delays of 2-5 minutes during peak usage times. If your 5-10 minute accuracy requirement is strict, build in monitoring to track actual delivery times. Set up alerts if webhooks are taking longer than expected, so you can failover to more aggressive polling if needed. Also, webhook payloads should include timestamps from the source system, not just when the webhook was sent, so you can measure true data freshness.
Don’t forget about data reconciliation for partial failures. If you’re batching records and one fails validation in AEC, you need logic to determine whether to retry just that record or the entire batch. We maintain a reconciliation table that tracks the sync status of each attendance record with fields like lastSyncAttempt, syncStatus, and errorMessage. This makes it easy to identify and retry failed records without reprocessing everything.
After implementing several event management integrations with AEC, I can share some battle-tested insights on all three aspects of your challenge.
Webhook vs Polling Trade-offs:
Webhooks provide the lowest latency (typically under 1 minute) and reduce unnecessary API calls, making them ideal for real-time scenarios. However, they introduce complexity around failure handling, retry logic, and security (verifying webhook signatures). Polling is simpler to implement and gives you complete control over timing and error recovery, but introduces inherent latency (minimum equals your polling interval) and can waste API quota checking for changes that haven’t occurred.
For your 200-500 attendee events with 5-10 minute accuracy requirements, I recommend the hybrid approach mentioned earlier. Configure webhooks as the primary delivery mechanism, but implement a polling-based reconciliation job every 10 minutes. This catches any webhook delivery failures while staying within your latency budget.
Data Reconciliation Strategy:
Implement a reconciliation framework with these components:
- Unique event IDs from source system that persist through both webhook and polling paths
- Timestamp tracking at three levels: event occurrence time, webhook delivery time, and AEC processing time
- A reconciliation table that stores the sync status of each attendance record
- Daily batch jobs that compare source system records against AEC data to identify discrepancies
For your burst check-in scenario, queue incoming webhooks and process them in batches of 25-30 records. This stays under AEC’s rate limits while maintaining good throughput. Tag each batch with a correlation ID for easier debugging.
Latency Handling Approach:
To meet your 5-10 minute freshness requirement:
- Configure webhook timeout at 30 seconds (fail fast)
- Implement exponential backoff retry: 10s, 30s, 60s, then route to polling
- Monitor webhook delivery latency with alerting at 3-minute threshold
- Run polling reconciliation every 10 minutes as backstop
- Use AEC’s bulk upsert API to handle both new records and updates in a single call
In our production deployments, this architecture achieves 95th percentile latency of 2-3 minutes for normal operations, with the polling safety net ensuring nothing is lost beyond the 10-minute window even during webhook outages. The key is treating webhooks as an optimization, not the only delivery path.