Industrial IoT (IIoT) is fundamentally different from consumer IoT. When you are monitoring an industrial manufacturing plant or a smart grid, you are dealing with PLCs and sensors that can output telemetry data at sub-second intervals. A standard REST API and a PostgreSQL database will collapse under the write-heavy load of a thousand devices streaming 50Hz data.
To solve this, SOLVEO architects specialized, high-throughput telemetry pipelines.
1. Ingestion: The Edge-to-Cloud Bridge
We standardize on MQTT for the edge-to-cloud link. MQTT’s publish/subscribe model is lightweight, supports Quality of Service (QoS) levels, and maintains persistent connections, significantly reducing the TCP handshake overhead inherent in HTTP. We typically deploy high-performance brokers like EMQX or Mosquitto clustered across availability zones.
2. Buffering and Stream Processing
Data must never be written directly to the database from the broker. During a database maintenance window or a sudden spike in network traffic, data would be lost.
Instead, the MQTT broker dumps payloads into an event streaming platform like Apache Kafka or Redpanda. Kafka acts as an immutable, distributed buffer. From here, stream processing engines (like Flink or custom Go-based consumers) pick up the data, clean it, aggregate it (e.g., creating 1-minute averages from raw 1-second data), and prepare it for storage.
3. Storage: Time-Series Databases
Time-series data requires specialized storage. Traditional relational databases index data in B-Trees, which become incredibly slow as the table size grows into the billions of rows.
We utilize TimescaleDB (a PostgreSQL extension) or InfluxDB. TimescaleDB automatically partitions data into “chunks” based on time ranges. This ensures that recent data—which is queried the most—remains entirely in fast RAM, while older data is compressed and pushed to disk.
By separating ingestion, buffering, and storage, we build systems that can reliably absorb massive telemetry spikes without dropping a single packet.