Best Practice

When talking about digitalization in the industry, security often comes up as one of the main concerns. Usually production environments are decoupled from other areas to prevent unauthorized actions. To realize this, a common practice is to separate networks into different segments like Production, OT, Office, Cloud. The concept of Node to Node communication comes in handy in these scenarios.

Examples of modules and solutions for making your data flow more resilient.

This article will provide you with some ideas how customers have implemented Crosser Node(s) in different infrastructures. Keep in mind that in all cases, the Node must be able to reach cloud.crosser.io via HTTPs port 443 in order to communicate.

The Databricks Publisher module is used to initiate transfers of files in a staging area into a Delta table. In order to use this module you first need to transfer your data to the staging area...

These modules can be used to send chat messages to Microsoft Teams. When sending chat messages you need to reference the destination chat by providing its ID. If you are using Microsoft Teams from your browser, you can find the chat ID in the URL when you open the relevant conversation.

Microsoft enable integration to Azure Event Grid via native MQTT, with Crosser you can therefore communicate directly without the need to go through the Azure IoT Hub. In addition this will allow you to use Crosser to integrate devices/assets from the edge side as well as services and applications on the cloud side.

Crosser offers two ways to run Python code from within a flow: the IronPython and the Python Bridge modules. The Crosser flow engine is built in .NET and Python is not a native .NET language. The IronPython module uses a Python interpreter running inside .NET. This means that all processing happens within .NET, which may improve performance...

One of the benefits of the Crosser solution is that you can deploy multiple flows (processes) into one existing container.

Due to that, you can add new use cases without influencing running processes at the edge, even without restarting or re-deploying the runtime (Docker Container or Windows Service).

This allows you to realize your use cases step by step and keep the processes isolated.

This article explains how to use metadata driven stream processing for performing calculations, validations and comparisons in your real-time data flows and pipelines.

In this article we describe what options you have and how to utilize provided interfaces for integration into your monitoring solution.