I’d like to start a discussion about dashboard latency trade-offs between real-time and batch data ingestion in SAP IoT. We’re designing a manufacturing monitoring dashboard and debating two approaches:
Real-time ingestion: MQTT events processed immediately, dashboard updates every 5 seconds
Batch ingestion: Data aggregated every 5 minutes, dashboard shows pre-calculated metrics
Real-time gives better user experience but increases system load. Batch processing is more efficient but introduces latency. What are your experiences with dashboard performance at scale? How do you balance responsiveness against system resources?
We went with real-time initially but had to switch to hybrid. Real-time for critical metrics (machine status, alarms) and 1-minute batch aggregation for historical trends. The key was identifying which metrics actually need sub-second updates. Most manufacturing KPIs don’t change meaningfully every 5 seconds, so batching them reduced our database load by 60% without impacting operational decisions.
Don’t forget about cost implications. Real-time ingestion with high-frequency updates costs more in terms of compute and storage I/O. We calculated that batch processing every 2 minutes vs real-time saved us 40% on cloud infrastructure costs while only adding acceptable latency for our use case. Run a cost-benefit analysis based on your specific SLAs.
Dashboard latency isn’t just about data ingestion frequency. The visualization rendering can be your bottleneck. We use real-time ingestion but refresh dashboards every 30 seconds. Between refreshes, we show a “live” indicator to users so they know data is current. This balances perceived responsiveness with actual browser performance - too many chart redraws cause UI lag.