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Crosser IoT Edge Streaming Analytics

Crosser
Visually develop flows with pre-built modules and multi-protocol connectors for Industrial IoT
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Crosser IoT Edge Streaming Analytics

Crosser

Visually develop flows with pre-built modules and multi-protocol connectors for Industrial IoT

With the Crosser edge computing solution running on IoT Edge you get a simple to use, yet powerful, streaming analytics engine that leverages the deployment tools in Azure while still maintaining the simplicity offered by the Crosser Cloud service.

Once deployed on the IoT Edge you configure the processing using the visual Flow Studio tool in the Crosser Cloud, where pre-built modules are connected into flows using a drag-and-drop interface. Any changes to your running flows is just a configuration change away, no need to re-deploy containers. Crosser Cloud has full version control of your flows and multi-edge operations making it really easy to update flows running on multiple edge devices.

You can use the Crosser IoT Edge module as a pure streaming analytics engine, taking data from the edge hub, process the data and then send the result back to the edge hub for further processing by other edge modules or for delivery to the Azure IoT hub. You can also use the Crosser I/O modules to get data directly from external sources (OPC UA, MQTT, Modbus, HTTP REST APIs etc.) and to send the results of your processing to other destinations than the Azure IoT hub (HTTP REST APIs, Mail, SMS, Slack etc.). A single flow can get data from multiple sources and deliver results to multiple destinations. You can also run multiple flows on each edge node.

The Crosser module library has an extensive list of both connector and compute modules that covers typical industrial IoT use cases. You can normalize your data by reformatting messages, add/remove metadata and scale values. Aggregate and filter your data to reduce the data volume and only send relevant data to the cloud. Define conditions for generating triggers and run your own Python or C# code, including machine learning models, using the code modules.

Minimum hardware requirements: Linux x64 100 MB of disk space, 100 MB of RAM.

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