Delineating IoT streaming analytics

Abstract

With the massive surge in the number of developed and deployed IoT devices and sensors in mission-critical environments such as smart homes, hospitals, hotels, warehouses, retail stores, railway stations, ports, etc., the speed, size, scope and structure of IoT data is evolving fast. In this chapter, we specifically focused on capturing, cleaning and crunching IoT streaming data in real time to extract actionable insights for producing real-time and real-world IT and business services and applications.Streaming data has become an important component of worldwide business houses. The enterprise data architecture has to take this trend into account in order to be futuristic and flexible. Due to the exponential growth of Internet of Things (IoT) devices and sensors, enterprises are keenly strategizing afresh to accommodate IoT data. Typically, IoT data and other data sources such as web applications, security logs, and device interactions are streaming data. Meticulously receiving and subjecting streaming data deftly and quickly to make sense/money out of it are being seen as a viable activity for any enterprise to keep up its edge over its competitors. Analysis of streaming data gives a huge understanding into timely and trustworthy insights for enterprises. Decision-makers and other senior management people can take both tactical and strategically sound decisions with all the clarity and confidence.In this chapter, we are going to dig deeper in order to explain how all sorts of IoT streaming data can be subjected to a variety of studies with the intention of emitting out actionable insights. Also, how the knowledge discovered gets disseminated to participating and contributing IoT devices to exhibit a kind of adaptive behavior. Also, the insights extracted can be supplied to senior management people to visualize and realize next-generation cognitive IoT products, solutions, and services.