Edge analytics holds the key to the future of data entry services because the current cloud computing capacity may not be sufficient to tackle the humungous amount of generated then. By the end of 2020, it is estimated that there will be there will be more than 20 billion connected devices across the globe, which will be around 3 connected devices per person. The data generated by these devices is something unimaginable and beyond the scope of the current cloud computing capacity.
In Edge Computing all data is not sent to the cloud but is processed near the source of data generation. For large-scale IoT deployments like as in oil rigs, mines or factories featuring low bandwidth, low latency environments, Edge can be a crucial because of the sheer volumes of data being generated.
In future, we may need slightly different processing models than cloud because the data generated in an enterprise network will be massive and the secondly the larger IoT devices will demand much lower latency than what current cloud computing can deliver. This is where edge computing comes into the picture. It will function as middleware layer instead of a cloud server with which the connected devices and sensors can communicate.
Edge will have computing resources that can process information and relay it back to the devices super-fast, which in turn will enable the users to perform immediately follow up actions. This instantaneous analysis of data and relay of results is the main feature of Edge computing model
Some of the other benefits of Edge includes the following
Edge is less expensive than cloud storage and computing models because it uses gateway devices that enable the functioning of enterprises to work with less constricted networks.
Edge computing model is standalone and can perform operations even in the event of one device suffering downtime.
Edge can work in sync with cloud computing where the edge will manage low-latency workloads while cloud will continue to be the storage and computing powerhouse. By bringing the computing workload closer to the edge will drastically cut down communication costs and ensure instant action.
A multitude of Analytics OS-enabled edge devices can thus work in conjunction with broader cloud-based analytics. While cloud-based models can optimize multiple locations at an enterprise level, Edge analytics could be used in the optimization of an individual entity.
However, the edge may not be effective in providing detailed insights into a system if it is not enhanced with a cloud-based data crunching system. Edge may be more suited for small enterprises because organizations that deal with the massive volume of IoT data may typically need both real-time analytics and detailed insights.
They can opt to run analytics models on the edge while the detailed IoT analytics and model development can be run in a cloud. It might require different efficacy models for edge analytics and cloud-based analytics. Edge analytics is about driving real-time decisions while cloud-based analytics is most suitable for fundamentally enhancing the IoT value chain.
IoT architects opine that whole Edge will bring their business to be as close to the data as possible it might have some limitations while running run analytics on edge devices in terms of memory or power consumption. They can address this issue by segregating analytics operations and execution, and creatively deploying these at the edge or in the cloud as needed.
There are various platforms like Azure stream and IBM Watson among others that can be used to optimize edge-based data analytics. Edge offers a less expensive method of data management and utilizes a gateway device instead of the busy cloud network and can operate as a stand-alone network. The benefits of Edge far outweigh its blemishes, which means that it will have a crucial role in the future of database management in organizations across the world in the days ahead.