Kersplody

IT Services and IT ConsultingColorado, United States2-10 Employees

Data Processing platforms assume data integration and processing at a central location for data generated at the edge. Data generated on edge devices is typically summarized then discarded, making it unavailable for analysis. This makes solutions essentially static and slow to adapt to changing analysis and operational needs. Networks are imperfect: dropped connections; low or variable bandwidth; long/large and variable latencies; and dynamic data paths.  This limits what can be transferred back to a central cloud for analysis. Since data rates are increasing exponentially, sensors are being deployed everywhere, and data generated is growing at a much higher rate than network throughput and high mobile data costs and restricted bandwidth limits the amount of data available for central analysis and decisioning. Additionally, real-time analysis and decisioning are cumbersome: current approaches limit a system’s ability to rapidly adapt to the world as data needs change, and labor Intensive: every time code is moved between technologies or devices, significant software redevelopment is required, dramatically increasing costs.

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