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Edge analytics refers to the analysis of data from some non-central point in a system, such as a network switch, peripheral node or connected device or sensor. As an emerging term, “edge analytics” defines the attempt to collect data in decentralized environments.
One way to understand edge analytics is as an alternative to traditional big data analytics, which is performed in centralized ways, through Hadoop clusters or other means, often from a big data warehouse or other central repository. This has been a popular way to drive analytics, but now, data scientists are exploring how edge analytics can work as an effective alternative option.
In some ways, edge analytics goes along with the internet of things (IoT). Experts often describe IoT data as inherently messy or chaotic. There is a need to find the best ways to collect data from distributed systems. Because there is so much work involved in sourcing device data into a central data warehouse, edge analytics has emerged as a time-saving and resource-saving option. Some describe edge analytics as “harnessing” the power of the connected IoT device: the idea is that analysts get the data right from the active device, and not later after it has been filtered into the warehouse. There is also the ability to filter data for long-term storage.
One prominent example of edge analytics is in the use of digitally connected traffic systems. A party, for instance, a law enforcement department, might want data like camera images or sensor speeds, in real time or before the data has trickled into a data warehouse for consistency. CCTV units and other endpoint devices can deliver timely data through edge analytics.