📡 IoT · Data Flow
IoT Telemetry Data Pipeline
How sensor readings travel from devices to dashboards and alerts: MQTT ingestion, stream processing, time-series storage and anomaly detection.
Drawing diagram…
What this diagram shows
- Devices publish over MQTT to a broker
- Stream processor enriches readings and spots anomalies
- Hot data goes to a time-series database, raw data to a data lake
Prompt used
Data flow for an IoT telemetry pipeline: sensors publish readings over MQTT to a broker, a stream processor (Apache Flink) validates and enriches them with device metadata from the device registry, writes them to a time-series database (InfluxDB) for dashboards and to a data lake for analytics, and sends anomalies to an alerting service that notifies operators.
Mermaid code
flowchart LR S[(Sensors)] -->|Readings over MQTT| B[MQTT Broker] B -->|Raw events| F[Stream Processor - Flink] R[(Device Registry)] -->|Device metadata| F F -->|Clean readings| T[(Time-series DB)] F -->|Raw archive| L[(Data Lake)] F -->|Anomalies| A[Alerting Service] T -->|Metrics| D[Dashboards - Grafana] A -->|SMS / email| O[Operators] L -->|Batch data| M[Analytics and ML]
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