Microsoft plugs Cisco Edge into Azure IoT Hub

by Don Sharpe
Don Sharpe
Don Sharpe
Don has been writing professionally for over 10 years now, but his passion for the written word started back in his elementary school days. His work has been... read more
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Microsoft recently announced the integration of Cisco Edge Intelligence with Azure IoT Hub to help users leverage IoT data much faster. This is excellent news to Azure customers that are building edge computing systems to power real-time applications like event-driven processing or data filtering.

Pre-integrated Edge to Azure IoT Hub

In a typical Azure IoT Hub deployment, organizations will be utilizing Cisco IoT network devices with pre-loaded software-based intelligence. They can leverage the integrated IoT solution to create applications that deliver telemetry data pipelines, said Microsoft.

We have decided to team up to share the availability of an integrated Azure IoT solution, that provides the necessary software, hardware, and cloud services that businesses need to rapidly launch IoT initiatives and quickly realize business value.

Already, Azure IoT users can extract spatial insights using Azure Maps, enabling them to get the exact location of tracked assets along with any other telemetric data, such as the status of important equipment. Now they can build advanced analytics with applications that leverage such IoT data at or near the source.

The Cisco Edge-Azure IoT Hub solution can power various services in Azure, two of which are:

Real-time analytics

One of the primary objectives of Microsoft Azure Stream Analytics is to give enterprises the ability to develop highly scalable and versatile data pipelines. Using the tool, big data analysts can derive in-depth insights into millions of events at incredibly low latencies. It is now a lot easier to achieve those goals with the incorporation of Cisco’s intelligence-driven edge-computing tech into Azure.

So, if you are an Azure Stream Analytics user, you can develop applications that leverage Cisco Edge Intelligence to analyze vast amounts of streaming data at subsecond latencies.

Rather than send mission-critical data to the cloud before processing, you may extract intelligence from it at the source. This way, you avoid latency issues that usually stall decision-making.

Machine learning (ML)

Implementing ML at the edge makes sense in AI applications that demand near real-time inference. One such use case is asset health predictive analytics.

The Microsoft-Cisco collaboration on IoT technology comes at time when networking technologies, such as 5G, are getting faster and IoT devices are collecting vast amounts of data. Azure IoT Hub users can tap into the resultant tech synergy to deliver real-time big data to applications that need it.


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