Data In Motion analytics and real-time data analytic

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3.8 Big Data and Stream Analytics
Along with volume and variety, as we have seen earlier in this chapter, one of the key characteristics that defines Big Data is velocity, which refers to the speed at which the data is created and streamed into the analytics environment. Organizations are looking for new means to process streaming data as it comes in to react quickly and accurately to problems and opportunities to please their customers and to gain a competitive advantage. In situations where data streams in rapidly and continuously, traditional analytics approaches that work with previously accumulated data (i.e., data at rest) often either arrive at the wrong decisions because of using too much out-of-context data, or they arrive at the correct decisions but too late to be of any use to the organization. Therefore, it is critical for a number of business situations to analyze the data soon after it is created and/or as soon as it is streamed into the analytics system.
The presumption that the vast majority of modern-day businesses are currently living by is that it is important and critical to record every piece of data because it might contain valuable information now or sometime in the near future. However, as long as the number of data sources increases, the “store-everything” approach becomes harder and harder and, in some cases, not even feasible. In fact, despite technological advances, current total storage capacity lags far behind the digital information being generated in the world. Moreover, in the constantly changing business environment, real-time detection of meaningful changes in data as well as of complex pattern variations within a given short time window is essential to come up with the actions that better fit with the new environment. These facts become the main triggers for a paradigm that we call stream analytics. The stream analytics paradigm was born as an answer to these challenges, namely, the unbounded flows of data that cannot be permanently stored to be subsequently analyzed, in a timely and efficient manner, and complex pattern variations that need to be detected and acted on as soon as they happen.
Stream analytics (also called data-in-motion analytics and real-time data analytics, among others) is a term commonly used for the analytic process of extracting actionable information from continuously flowing/streaming data. A stream is defined as a continuous sequence of data elements (Zikopoulos et al., 2013). The data elements in a stream are often called tuples. In a relational database sense, a tuple is similar to a row of data (a record, an object, an instance). However, in the context of semistructured or unstructured data, a tuple is an abstraction that represents a package of data, which can be characterized as a set of attributes for a given object. If a tuple by itself is not sufficiently informative for analysis or a correlation—or other collective relationships among tuples are needed—then a window of data that includes a set of tuples is used. A window of data is a finite number/sequence of tuples, where the windows are continuously updated as new data become available. The size of the window is determined based on the system being analyzed. Stream analytics is becoming increasingly more popular because of two things. First, time-to-action has become an ever-decreasing value, and second, we have the technological means to capture and process the data while it is created.
Some of the most impactful applications of stream analytics were developed in the energy industry, specifically for smart grid (electric power supply chain) systems. The new smart grids are capable of not only real-time creation and processing of multiple streams of data to determine optimal power distribution to fulfill real customer needs, but also generating accurate short-term predictions aimed at covering unexpected demand and renewable energy generation peaks. Figure 3.10 shows a depiction of a generic use case for streaming analytics in the energy industry (a typical smart grid application). The goal is to accurately predict electricity demand and production in real time by using streaming data that is coming from smart meters, production system sensors, and meteorological models. The ability to predict near future consumption/production trends and detect anomalies in real time can be used to optimize supply decisions (how much to produce, what sources of production to use, and optimally adjust production capacities) as well as to adjust smart meters to regulate consumption and favorable energy pricing.

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