High-throughput time-series data ingestion stream built for monitoring regionally distributed industrial assets.
The client gathered real-time operational telemetry from thousands of remote industrial IoT devices. However, massive packet spikes during peak hours regularly overwhelmed their traditional database structure, leading to data dropouts, sluggish query times, and delays in recognizing industrial equipment errors.
Establishing an elastic buffer and rapid ingestion queue was vital to safeguard telemetry fidelity and operational scheduling data.
Norexter Solutions designed a scalable, high-throughput ingestion architecture. We built a custom ingestion microservice in Go (Golang) to leverage its excellent concurrency performance. We introduced Apache Kafka to buffer burst loads, and selected InfluxDB for time-series optimized write speeds and storage compaction.
This technical choice provides high resiliency during sudden data surges and scales comfortably as more physical nodes are online.
The telemetry pipeline automatically validates schemas on all incoming payloads. Validated parameters are dispatched straight to InfluxDB for analytics queries, while an isolated event processor monitors live telemetry feeds for threshold violations, sending immediate alerts to dispatch operators.
The Go-based pipeline processes large-scale sensor streams smoothly without packet loss, resolving analytical queries in less than 50ms.
