A FRAMEWORK FOR REAL-TIME STREAMING ANALYTICS USING MACHINE LEARNING APPROACH
D. Jayanthi, G. Sumathi · COMPUSOFT: An International Journal of Advanced Computer Technology · 2016
The continuous stream of data generated by sensors, machines, vehicles, mobile phones, social media networks, and other real-time sources are compelling organizations to imagine what they could do with this data if they could gain insight into it. A real-time streaming platform must meet the needs of data scientists, developers and data center operations teams without requiring extensive custom code or brittle integration of many third party components. As more and more data is generated and collected, data analysis requires scalable, flexible, and high performing tools to provide insights in a timely fashion. However, organizations are facing a growing big data ecosystem where new tools emerge and “die†very quickly. Therefore, it can be very difficult to keep pace and choose the right tools. This paper focuses on the challenges that stream processing solution should address. Also, this paper analyzes the traditional analytic tools to bridge the gap between data being generated and data that can be analyzed effectively.