Reliably process late-arriving vehicle IoT data via MQTT
This architecture blueprint demonstrates how you can:
- Process real-time vehicle telemetry data with latency guarantees
- Handle out-of-order data from vehicles with unreliable connectivity
- Set up reliable alerting based on vehicle error codes
- Sink data at different levels of aggregation granularity into a time series database or data warehouse
Main project components
AWS IoT Core MQTT Broker
Receive and buffer MQTT data from remote devices.
Message normalizer
Convert varied message formats into a standardized structure. Supports both sensor readings and binary payloads
Windowed aggregations
Calculate vehicle performance metrics with support for custom aggregation windows. Process data with configurable time windows, handling late-arriving data with configurable grace periods.
Alerts
Monitor vehicle error codes and sensor thresholds, generating notifications when conditions are met. Send alerts about predicted issues.
Output connectors
Write processed metrics to time series databases with automatic batching and backpressure handling. Sink processed data into a data warehouse with configurable batch sizes.
Technologies used
Using this template
This template serves as an architecture blueprint for processing vehicle IoT data based on the Quix platform.
In includes:
- Late data handling with configurable grace periods
- Support for both streaming and batch workloads
- Resource scaling based on data volume
- Automated batch size optimization
- Built-in monitoring of processing latency
- Python-native stream processing
If you’re interested in implementing this architecture, get in touch.
