Thinxtream Azure IoT Hub
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Thinxtream can rapidly enable
your Connected Products and Smart Services business
with Azure IoT Hub

LEVERAGE OUR IN-DEPTH AZURE IOT HUB EXPERTISE TO BUILD YOUR IOT SOLUTIONS


AZURE IOT HUB

  • Communications using Messaging, File transfers and Request-Reply methods
  • Declarative Message Routing for sending device-to-cloud messages to other Microsoft services like Azure Stream Analytics, Azure Functions, Azure Service Bus or Azure Storage account
  • Communications using Messaging, File transfers and Request-Reply methods
  • Declarative Message Routing for sending device-to-cloud messages to other Microsoft services like Azure Stream Analytics, Azure Functions, Azure Service Bus or Azure Storage account
  • Integration with Azure Service Bus and Azure Logic Apps for Enterprise Integration and Analytics using tools like Microsoft Power BI®
  • Device Twins for a query-able store for device metadata and synchronized state information using JSON
  • Secure communications using X.509 certificates and access control at a per-device level
  • Monitoring for device connectivity using Operations Log and Activity Log
  • Device identity management using Azure IoT Hub Identity Registry
  • Support for AMQP, MQTT, HTTPS, or custom protocols for proprietary devices
  • Automatic, Zero-touch, Just-in-time provisioning of millions of devices using IoT Hub Device Provisioning Service
  • Field Gateway support for IoTification of low power devices, and devices without internet connectivity
  • Develop IoT solutions in C#, Java, Node.js, .NET Core, etc. and for various platforms (Linux®, RTOS, Windows®)
  • Use Azure IoT Edge, a Microsoft IoT service, to analyze device data and run business logic on the devices
    • Perform time sensitive decisions on device to reduce latency
    • Provide device management services, protocol translations, and enforce security
    • Deploy and run trained models from Azure Machine Learning Studio as Docker Containers on the Azure IoT Edge devices
    • Run Azure Functions and Azure Stream Analytics for real-time processing of incoming data on the field gateway or on the IoT Edge device
    • Develop business logic in Java®, .Net Core 2.0, Node.js, C and Python™
    • Build, test and deploy Deep Learning and AI solutions with VS Code extension
    • Integrate Apache Spark™ Machine Learning Pipelines with Microsoft Cognitive Toolkit and OpenCV library
    • Deploy Azure Machine Learning model as Web service
    • Pre-trained models based on Residual Deep Convolutional Networks for Transfer Learning


AZURE IOT HUB CASE STUDY