From 58bf909063b9ff1682cb72bf280b2e30c3b09cb1 Mon Sep 17 00:00:00 2001 From: Steve Busby Date: Thu, 24 Oct 2019 12:52:15 -0500 Subject: [PATCH] fixed link to Kepware's IoT Hub instructions --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d2a5a64..505670c 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ These instructions provide the necessary steps to connect PTC/Kepware's KEPServe ## Overview -PTC/Kepware's KEPServerEX is an industry leader in industrial and manufacturing device connectivity. It has connectivity libraries for a vast array of equipment and is a popular choice for unlocking the data from both new and legacy industrial devices. Kepware provides an [IoT Gateway](https://www.kepware.com/en-us/products/KEPServerEX/advanced-plug-ins/iot-gateway/) module today that, per their own [instructions](https://www.kepware.com/getattachment/c93c65df-57ea-4e9c-a1e0-2e9a34381d54/mqtt-client-and-microsoft-azure-iot.pdf), can be used to connect to and send data to Azure IoT Hub over the MQTT protocol. +PTC/Kepware's KEPServerEX is an industry leader in industrial and manufacturing device connectivity. It has connectivity libraries for a vast array of equipment and is a popular choice for unlocking the data from both new and legacy industrial devices. Kepware provides an [IoT Gateway](https://www.kepware.com/en-us/products/KEPServerEX/advanced-plug-ins/iot-gateway/) module today that, per their own [instructions](https://www.kepware.com/getattachment/64c9f1c6-b1df-421f-a59d-68814f5759e8/iot-gateway-mqtt-client-microsoft-azure.pdf), can be used to connect to and send data to Azure IoT Hub over the MQTT protocol. Many customers have expressed a desire to be able to send the data from KEPServerEX through Azure IoT Edge. That allows both for Edge to act as a gatewey through which traffic must flow, thus isolating the KEPServerEX further from the Internet, as well as the ability to take advantage of the many IoT Edge capabilities for pre-processing that data on the edge. Examples include the ability to use custom modules or Azure Functions to transform the data, Azure Streaming Analytics to do filtering and aggregation of the data before it goes to the cloud, and Azure Machine Learning for making predictions on the edge, potentially without having to send all of the detailed data to the cloud.