The Era of Analytics

Analytics a few months back was like the sensor data consolidated in a data warehouse or server and the analytics report was generated by processing the consolidated data. There are 2 problems with this approach:

1. Too much time to make a decision. The data shall traverse all the way from the source through gateways, routers, etc., and reach the data warehouse. The analytics engine crunch the data in steps involving data loading, data cleansing, data filtering, and passing it through the model.
2. The server becomes a single point of failure. If the server fails, data aggregation is halted; new analytics cannot be generated.

The world is moving into a stage where more or less every single device would be connected to a web network. In the next couple of years, more than 30 billion connected devices would be on the market. More devices mean more data and thus a prospective of making better and more precise business decisions.  The need of the hour is a distributed analytics network that is run closer to the sensors and end devices.  IoT gateways can be used to do more complex analytics with the data from various devices

Real-time data and Edge Analytics

One of the major shortcomings of the Hadoop framework is that it is unable to handle real-time data. Edge analytics using IoT gateways follow a distributed analytic strategy to reduce the effort associated with centralized analytics like big data analysis. The analysis job has been distributed to the edge devices which will deliver the relevant data to the central device by matching the traffic load. Through this method, the workload as well as the data traffic can be reduced.

Since the analysis part has been distributed across the gateway devices on the edge, the whole system will not fail even when the central device fails. The Edge device can handle some predictive jobs without having the full coordination of the central device thus making the sub-systems autonomous. By introducing the analytic power to the edge device; feed-forward and adaptive controlling can be incorporated with the help of Edge analytics and the whole network becomes robust and more transparent.

One of the main usages of edge analytics would be in traffic control systems. This helps authority curb traffic violations as data is available readily and in real-time rather than the delay caused due to the data moving to the warehouse for checking the consistency. In one of our earlier blogs, we mentioned about Azure and Intel IoT gateways. As experts in IoT gateway development, we at WeMakeIoT have experience and expertise in delivering projects based on Edge Analytics.