Real-Time IoT Data Processing with SQL

IoT data processing requires handling millions of sensor readings per second, detecting anomalies in real time, and maintaining device state dashboards. A streaming database like RisingWave processes IoT events with SQL materialized views — no custom code, no complex event processing frameworks.

IoT Streaming Pattern

CREATE SOURCE sensors (device_id VARCHAR, metric VARCHAR, value DECIMAL,
  reading_time TIMESTAMP WITH TIME ZONE)
WITH (connector='kafka', topic='iot-sensors', properties.bootstrap.server='kafka:9092')
FORMAT PLAIN ENCODE JSON;

-- Device health dashboard
CREATE MATERIALIZED VIEW device_health AS
SELECT device_id,
  AVG(value) FILTER (WHERE metric='temperature') as avg_temp,
  MAX(value) FILTER (WHERE metric='temperature') as max_temp,
  COUNT(*) as readings_1h,
  MAX(reading_time) as last_seen
FROM sensors WHERE reading_time > NOW() - INTERVAL '1 hour'
GROUP BY device_id;

-- Anomaly detection
CREATE MATERIALIZED VIEW anomalies AS
SELECT device_id, metric, value, reading_time
FROM sensors s
JOIN device_health d ON s.device_id = d.device_id
WHERE s.metric = 'temperature' AND s.value > d.avg_temp * 1.5;

Why SQL for IoT?

IoT teams are often hardware/firmware engineers, not Java developers. SQL is accessible to anyone. RisingWave's PostgreSQL compatibility means existing dashboarding tools (Grafana, Metabase) connect directly.

Frequently Asked Questions

Can SQL handle millions of IoT events per second?

Yes. RisingWave is a distributed system that scales horizontally. Materialized views distribute computation across multiple nodes, handling millions of events per second with sub-second latency.

How do I handle late IoT data?

Use watermarks to define acceptable lateness. IoT devices often buffer readings and send them in bursts. A watermark of 30-60 seconds handles most IoT lateness patterns.