This use case implements a Remote Sensor Data Aggregation Application that collects sensor data from multiple remote facilities and centralizes it in the cloud. The application receives sensor readings, validates and aggregates the data, and transfers it to the cloud for centralized storage, real-time monitoring, and historical analytics.
To implement a real-time sensor data aggregation architecture that collects data from distributed remote facilities and provides centralized cloud-based analytics.
Collect temperature, pressure, humidity, energy, and other sensor readings from remote facilities.
Python services receive sensor readings through MQTT from sensors and local gateways.
Validate incoming sensor data and identify invalid or missing readings.
The application checks sensor values, timestamps, device IDs, and data formats before storing or forwarding the data.
Combine sensor readings from multiple devices and facilities.
Kafka streams sensor events, while Python services aggregate readings based on facility, device, and time period.
Process aggregated sensor data in the cloud.
Spark performs transformation, cleaning, aggregation, and large-scale processing of centralized sensor datasets.
Store sensor data for real-time and historical analysis.
Processed sensor data is stored in Parquet format and maintained in S3 for efficient analytical access.
Analyze sensor data across facilities.
Trino performs SQL-based analysis, while Superset provides dashboards and reports for facility and sensor-level analytics.
Monitor sensor-data pipelines and infrastructure.
Prometheus collects application and infrastructure metrics, while Grafana displays real-time monitoring dashboards.
Provides compute resources for cloud-based sensor aggregation and analytics services.
Stores aggregated sensor datasets and historical data.
Provides the secure network environment for cloud workloads.
Provides persistent storage for application and database workloads.
Manages access permissions for cloud resources.
Controls network traffic and protects cloud resources.
Transfers sensor readings from remote facilities to data-collection services.
Streams sensor events from multiple facilities for real-time aggregation.
Performs large-scale sensor-data processing and transformation.
Stores processed sensor data efficiently for analytics.
Stores sensor information, device details, facility metadata, and aggregated records.
Queries and analyzes distributed sensor datasets.
Provides centralized sensor analytics dashboards and reports.
Collects application and infrastructure metrics.
Displays real-time pipeline and infrastructure monitoring.
Packages sensor aggregation and analytics services into containers.
Deploys, manages, and scales containerized cloud workloads.
Automates cloud infrastructure provisioning.
Automates configuration and deployment across remote and cloud environments.
The proposed solution uses a distributed sensor aggregation architecture that collects data from multiple remote facilities and centralizes it in the cloud. MQTT receives sensor readings, Kafka manages real-time sensor events, and Python services perform validation and aggregation. Spark processes large datasets, while Parquet and S3 provide analytical storage. Trino and Superset support centralized sensor analytics, and Prometheus/Grafana provide monitoring. This architecture provides real-time sensor aggregation with centralized cloud analytics while supporting multiple remote facilities.
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