Section 7.8 Review Questions 1. What are the most fruitful industries for stream analytics? lany industries can benefit from stream analytics. Some prominent examples include e-commerce, telecommunications, law enforcement, cyber security, the power industry, health sciences, and the government How can stream analytics be used in e-commerce? Companies such as Amazon and e Bay use stream analytics to analyze customer behavior in real time. Every page visit, every product looked at, every search conducted, and every click made is recorded and analyzed to maximize the value gained from a user's visit. Behind the scenes, advanced analytics are crunching the real-time data coming from our clicks. and the clicks of thousands of others to "understand what it is that we are interested in (in some cases, even we do not know that)and make the most of that information by creative offerings In add ition to what is listed in this section, can you think of other industries and/or application areas where stream analytics can be used? Stream analytics could be of great benefit to any industry that faces an influx of relevant real-time data and needs to make quick decisions. One example is the news industry. By rapidly sifting through data streaming in, a news organization can recognize"newsworthy"themes (i.e, critical events). Another benefit would be for weather tracking in order to better predict tornadoes or other natural disasters. Different students will have different answers. 4. Compared to regular analytics, do you think stream analytics will have more (or less) use cases in the era of Big Data analytics? Why? Stream analytics can be thought of as a subset of analytics in general, just like may refer to traditional data warehousing approaches. which does constrain the n regular"analytics. The question is, what does"" mean? Regular analytics types of data sources and hence the use cases. Or, "regular"may mean analytics on any type of permanent stored architecture(as opposed to transient streams ). I this case, you have more use cases for"regular"(includ ing Big Data) than in the previous definition. In either case, there will probably be plenty of times when regular"use cases will continue to play a role, even in the era of Big Data analytics. Different students will have d ifferent answers. Copyright C2018 Pearson Education, Inc.10 Copyright © 2018Pearson Education, Inc. Section 7.8 Review Questions 1. What are the most fruitful industries for stream analytics? Many industries can benefit from stream analytics. Some prominent examples include e-commerce, telecommunications, law enforcement, cyber security, the power industry, health sciences, and the government. 2. How can stream analytics be used in e-commerce? Companies such as Amazon and eBay use stream analytics to analyze customer behavior in real time. Every page visit, every product looked at, every search conducted, and every click made is recorded and analyzed to maximize the value gained from a user’s visit. Behind the scenes, advanced analytics are crunching the real-time data coming from our clicks, and the clicks of thousands of others, to “understand” what it is that we are interested in (in some cases, even we do not know that) and make the most of that information by creative offerings. 3. In addition to what is listed in this section, can you think of other industries and/or application areas where stream analytics can be used? Stream analytics could be of great benefit to any industry that faces an influx of relevant real-time data and needs to make quick decisions. One example is the news industry. By rapidly sifting through data streaming in, a news organization can recognize “newsworthy” themes (i.e., critical events). Another benefit would be for weather tracking in order to better predict tornadoes or other natural disasters. (Different students will have different answers.) 4. Compared to regular analytics, do you think stream analytics will have more (or less) use cases in the era of Big Data analytics? Why? Stream analytics can be thought of as a subset of analytics in general, just like “regular” analytics. The question is, what does “regular” mean? Regular analytics may refer to traditional data warehousing approaches, which does constrain the types of data sources and hence the use cases. Or, “regular” may mean analytics on any type of permanent stored architecture (as opposed to transient streams). In this case, you have more use cases for “regular” (including Big Data) than in the previous definition. In either case, there will probably be plenty of times when “regular” use cases will continue to play a role, even in the era of Big Data analytics. (Different students will have different answers.)