Q: How to implement time-series data storage and analytics for IoT sensor data?
Answer
For IoT time-series storage: (1) InfluxDB: purpose-built time-series DB with InfluxQL and Flux query languages. Built-in retention policies downsampling and continuous queries. Native Telegraf collectors for common protocols. (2) TimescaleDB: PostgreSQL extension for time-series – use SQL joins with relational data. (3) Apache IoTDB: designed for IoT with array-based storage efficient for burst writes. For edge storage use SQLite with timeseries extension. Data pipeline: sensor -> MQTT broker (Mosquitto) -> Telegraf -> InfluxDB. Analytics: (1) Grafana dashboards for real-time monitoring. (2) Kapacitor for alerting (threshold breach triggers email/SMS/webhook). (3) Python/R scripts for ML-based anomaly detection (isolation forest scikit-learn). (4) Apache Spark for batch analytics on historical data. Compress old data with downsampling (1 min -> 1 hour -> 1 day).
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