Our team is designing a telemetry analytics pipeline for Watson IoT Platform and debating between real-time stream processing versus batch analytics approaches using the data stream SDK. We have 300 industrial sensors generating temperature, pressure, and vibration data every 10 seconds. Real-time processing would enable immediate alerting for anomalies, but concerns about scalability and latency exist. Batch analytics running hourly would be more resource-efficient and easier to maintain, but delays critical alert notifications. I’m interested in hearing from teams who’ve implemented either approach - particularly regarding processing efficiency, alert accuracy, and whether hybrid processing (real-time for alerts, batch for historical analysis) is worth the added complexity. What are the practical performance differences, and how do you balance immediate insights versus computational costs?
Real-time alerts are essential for safety-critical applications. We use stream processing for immediate anomaly detection (temperature exceeds threshold, vibration spike detected) and batch analytics for trend analysis and predictive maintenance models. The hybrid approach works well - real-time catches urgent issues within seconds, batch jobs run overnight to identify gradual degradation patterns. The data stream SDK supports both patterns through different consumption modes.
We started with pure real-time processing and quickly hit scalability issues. At 300 devices × 6 readings/minute, that’s 1,800 events per minute. Real-time anomaly detection on every event consumed significant compute resources. Our Lambda functions were timing out during peak loads. We switched to micro-batching - process events in 30-second windows. This reduced compute costs by 60% while maintaining acceptable alert latency for our use case.
Don’t underestimate operational complexity of hybrid systems. You’re maintaining two separate processing pipelines, monitoring infrastructure, and alert delivery mechanisms. For our manufacturing environment, we found that well-tuned batch processing every 15 minutes provided sufficient responsiveness for most scenarios. Only truly critical sensors (safety systems, high-value equipment) justify real-time processing overhead. Evaluate your actual alert response time requirements before committing to real-time complexity.