IoT Data Platform Architecture

What this diagram shows

This diagram shows how IoT devices collect and transmit telemetry data through a message broker into a platform that separates real-time processing from long-term storage. Data flows from edge devices via MQTT into an ingestion layer, then splits into hot storage for immediate analytics and anomaly detection, and cold storage for historical archive. A separate management channel handles device configuration and firmware updates. The result is actionable dashboards fed by both live metrics and batch analysis, with feedback loops for alerting and OTA commands back to devices.

Key components

• IoT Devices — Sensors and actuators that publish timestamped measurements to MQTT topics at regular intervals or on state change.
• MQTT Broker — Receives, validates and routes all incoming telemetry messages from devices to consumer applications without transformation.
• Stream Processing Engine — Consumes MQTT messages in real time, applies windowing and aggregations, and feeds data to dashboards and anomaly detection.
• Time-Series Database — Stores recent metric data with microsecond precision and efficient range queries for dashboard queries and alerting.
• Cold Storage — Archives historical data to object storage or data warehouse for compliance, trend analysis and re-processing.
• Anomaly Detection Service — Monitors streaming data against thresholds or statistical baselines and triggers alerts when deviations occur.
• Device Management Service — Handles firmware updates, configuration pushes and command execution on devices via MQTT or dedicated endpoints.

When to use it

Use this template when designing a system that ingests high-volume sensor or meter data, requires both real-time dashboarding and long-term audit trails, and needs to push updates back to devices. It works well for industrial monitoring, smart building systems, connected vehicle fleets, and environmental sensor networks where you must separate operational intelligence from compliance storage and device lifecycle management is critical.

Common mistakes

• Putting all data into hot storage without archival strategy, causing cost escalation and query slowdown after weeks of operation.
• Designing anomaly detection rules without baselining normal device behaviour first, resulting in alert fatigue or missed genuine faults.
• Assuming MQTT QoS 0 is sufficient for critical telemetry; devices often disconnect and lose messages without QoS 1 or 2 persistence.

Adapting it to your system

Replace MQTT broker with your chosen transport (Kafka, Azure Event Hub, Google Pub/Sub) and adjust topic structure to match your device taxonomy. Substitute the stream processor with your platform's native option (Flink, Spark, Kafka Streams). Select a time-series store matching your query patterns and retention budget (InfluxDB, TimescaleDB, Prometheus). Define anomaly thresholds and windows based on baseline behaviour from existing devices. Map your device firmware and config schema into the OTA management service, and version control all update payloads.