Edge analytics is defined as the analysis of data gathered from a non-central point in a system, such as a sensor, network switch, or peripheral node. Analysis in big data analytics is performed in centralized ways through big data centers, central depository, or Hadoop clusters. The principle behind edge analytics is that the analysts gather data directly from active devices eliminating the need to send the entire data to a central warehouse, thereby saving time and resources. Analytic algorithms designed at the edge of a corporate network decide which information is worth sending to the cloud or central data storage depository for later use. In a number of industries, such as mining, oil and gas, renewable energy, telecom and manufacturing, data transmission from industrial equipment, machines, and other remote devices connected to the Internet of things (IoT) burdens operational data, which can be difficult and expensive to manage. Edge analytics devices are gaining popularity across a number of industries due to the necessity to act on data in real-time, which is close to the source, in order to ensure continuous operation of devices and sensors.

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The edge analytics solutions are primarily responsible for collecting, cleansing, integrating, and filtering data from sensors and devices. System scalability, cost optimization, and increase in penetration of smart connected devices are some key drivers of the edge analytics market. Development of new technologies, such as machine learning, visualization and Internet of Everything (IoE), which connect devices from retail cameras to the Internet and industrial sensors to wearable create opportunity in the edge analytics market. Users can make important decisions quickly with the help of IoE by predicting the future outcomes after analyzing present data based on previously available data. Providing analytics to the edge of the network requires new network management capabilities, processing requirements, and data flows. The main restraint for the market is lack of efficient real-time algorithms, which can process real-time data and serve real-time applications.

The global edge analytics market can be segmented based on component, type, deployment model, end-use industry, and region. Based on component, the edge analytics market can be bifurcated into solutions and services. In terms of type, the edge analytics market can be segregated into diagnostic analytics, descriptive analytics, predictive analytics, and prescriptive analytics. Based on deployment model, the edge analytics market can be divided into on-premise and on-cloud. In terms of end-use industry, the edge analytics market can be categorized into health care and life sciences, banking financial services and insurance (BFSI), media & entertainment, energy & utility, manufacturing, transportation & logistics, retail & consumer goods, IT & telecommunication, government & defense, travel & hospitality, and others. In terms of region, the edge analytics market can be divided into North America, Asia Pacific, Europe, Middle East & Africa, and South America. Proliferation of various mobile devices in the North America is expected to drive the edge analytics market in the region during the forecast period. Furthermore, growing adoption of wireless mobile communications technologies, such as 3G and 4G, is expected to fuel the edge analytics market in Asia Pacific during the forecast period.

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Key players operating in the global edge analytics market include Cisco Systems Inc., Oracle Corporation, SAP SE, Apigee Corporation, SAS Institute Inc., AGT International Inc., PrismTech Corporation, Greenwave Systems, CGI Group Inc., Dell Technologies Inc, Foghorn Systems, PrismTech Corporation, Equinix, Inc., and Intel Corporation.

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