Devices: Devices can be connected to the cloud directly or indirectly. It is done directly using IP-capable devices that establish secure connections via the internet. In the indirect method, devices connect via a field gateway. This enables aggregation and reduced raw device data before transporting the back-end and local decision-making capability.
IoT Hub: Azure IoT Hub offers built-in, high-scale, secure connectivity, data, and event ingestion. It also establishes bi-directional communication with devices, including device management with command and control capabilities. Azure IoT Hub can securely connect millions of devices to the cloud from various devices and protocols.
Stream Analytics: To process the massive amount of data generated by the field devices in real-time and process this live stream, Azure Stream Analytics is used.
Event Hub: Azure Stream Analytics generates an event and sends it to the Event Hub, which triggers background jobs for further analysis.
Event Processor: Web Job is used to process the event data received from Event Hub, and a portion of this data is used to train in the ML model.
Storage: Storage is divided into warm and cold path stores. Warm path data is required to be available for reporting and immediate visualization from devices. Cold path data is stored for the long term and used for batch processing. We use Azure Cosmos DB for warm path storage and Azure Blob for cold path stores.
Dashboard and UI- Web App or Power BI is used to create and show the dashboard to the end-user to engage in interactive browsing.
Machine Learning: It enables systems to learn from historical data and to act without being explicitly programmed. Scenarios such as predictive maintenance are enabled through ML. We use Azure Machine Learning for ML needs.