Chapter 6 Big Data and Analytics

Chapter 6 Big Data and Analytics 1) In the opening vignette, the CERN Data Aggregation System (DAS), built on MongoDB (a Big Data management infrastructure), used relational database technology. Answer: FALSE Diff: 2 Page Ref: 277 2) The term "Big Data" is relative as it depends on the size of the using organization. Answer: TRUE Diff: 2 Page Ref: 279 3) In the Luxottica case study, outsourcing enhanced the ability of the company to gain insights into their data. Answer: FALSE Diff: 2 Page Ref: 283-284 4) Many analytics tools are too complex for the average user, and this is one justification for Big Data. Answer: TRUE Diff: 2 Page Ref: 284 5) In the investment bank case study, the major benefit brought about by the supplanting of multiple databases by the new trade operational store was providing real-time access to trading data. Answer: TRUE Diff: 2 Page Ref: 288 6) Big Data uses commodity hardware, which is expensive, specialized hardware that is custom built for a client or application. Answer: FALSE Diff: 2 Page Ref: 289 7) MapReduce can be easily understood by skilled programmers due to its procedural nature. Answer: TRUE Diff: 2 Page Ref: 291 8) Hadoop was designed to handle petabytes and extabytes of data distributed over multiple nodes in parallel. Answer: TRUE Diff: 2 Page Ref: 291 9) Hadoop and MapReduce require each other to work. Answer: FALSE Diff: 2 Page Ref: 295 1 1/13 10) In most cases, Hadoop is used to replace data warehouses. Answer: FALSE Diff: 2 Page Ref: 295 11) Despite their potential, many current NoSQL tools lack mature management and monitoring tools. Answer: TRUE Diff: 2 Page Ref: 295 12) The data scientist is a profession for a field that is still largely being defined. Answer: TRUE Diff: 2 Page Ref: 298 13) There is a current undersupply of data scientists for the Big Data market. Answer: TRUE Diff: 2 Page Ref: 300 14) The Big Data and Analysis in Politics case study makes it clear that the unpredictability of elections makes politics an unsuitable arena for Big Data. Answer: FALSE Diff: 2 Page Ref: 301 15) For low latency, interactive reports, a data warehouse is preferable to Hadoop. Answer: TRUE Diff: 2 Page Ref: 306 16) If you have many flexible programming languages running in parallel, Hadoop is preferable to a data warehouse. Answer: TRUE Diff: 2 Page Ref: 306 17) In the Dublin City Council case study, GPS data from the city's buses and CCTV were the only data sources for the Big Data GIS-based application. Answer: FALSE Diff: 2 Page Ref: 309-310 18) It is important for Big Data and self-service business intelligence go hand in hand to get maximum value from analytics. Answer: TRUE Diff: 1 Page Ref: 313 19) Big Data simplifies data governance issues, especially for global firms. Answer: FALSE Diff: 2 Page Ref: 313 2 2/13 20) Current total storage capacity lags behind the digital information being generated in the world. Answer: TRUE Diff: 2 Page Ref: 315 21) Using data to understand customers/clients and business operations to sustain and foster growth and profitability is A) easier with the advent of BI and Big Data. B) essentially the same now as it has always been. C) an increasingly challenging task for today's enterprises. D) now completely automated with no human intervention required. Answer: C Diff: 2 Page Ref: 279 22) A newly popular unit of data in the Big Data era is the petabyte (PB), which is A) 109 bytes. B) 1012 bytes. C) 1015 bytes. D) 1018 bytes. Answer: C Diff: 2 Page Ref: 281 23) Which of the following sources is likely to produce Big Data the fastest? A) order entry clerks B) cashiers C) RFID tags D) online customers Answer: C Diff: 2 Page Ref: 281-282 24) Data flows can be highly inconsistent, with periodic peaks, making data loads hard to manage. What is this feature of Big Data called? A) volatility B) periodicity C) inconsistency D) variability Answer: D Diff: 2 Page Ref: 282 25) In the Luxottica case study, what technique did the company use to gain visibility into its customers? A) visibility analytics B) data integration C) focus on growth D) customer focus Answer: B Diff: 2 Page Ref: 283-284 3 3/13 26) Allowing Big Data to be processed in memory and distributed across a dedicated set of nodes can solve complex problems in near—real time with highly accurate insights. What is this process called? A) in-memory analytics B) in-database analytics C) grid computing D) appliances Answer: A Diff: 2 Page Ref: 286 27) Which Big Data approach promotes efficiency, lower cost, and better performance by processing jobs in a shared, centrally managed pool of IT resources? A) in-memory analytics B) in-database analytics C) grid computing D) appliances Answer: C Diff: 2 Page Ref: 286 28) How does Hadoop work? A) It integrates Big Data into a whole so large data elements can be processed as a whole on one computer. B) It integrates Big Data into a whole so large data elements can be processed as a whole on multiple computers. C) It breaks up Big Data into multiple parts so each part can be processed and analyzed at the same time on one computer. D) It breaks up Big Data into multiple parts so each part can be processed and analyzed at the same time on multiple computers. Answer: D Diff: 3 Page Ref: 291 29) What is the Hadoop Distributed File System (HDFS) designed to handle? A) unstructured and semistructured relational data B) unstructured and semistructured non-relational data C) structured and semistructured relational data D) structured and semistructured non-relational data Answer: B Diff: 2 Page Ref: 291 30) In a Hadoop "stack," what is a slave node? A) a node where bits of programs are stored B) a node where metadata is stored and used to organize data processing C) a node where data is stored and processed D) a node responsible for holding all the source programs Answer: C Diff: 2 Page Ref: 292 4 4/13 31) In a Hadoop "stack," what node periodically replicates and stores data from the Name Node should it fail? A) backup node B) secondary node C) substitute node D) slave node Answer: B Diff: 2 Page Ref: 292 32) All of the following statements about MapReduce are true EXCEPT A) MapReduce is a general-purpose execution engine. B) MapReduce handles the complexities of network communication. C) MapReduce handles parallel programming. D) MapReduce runs without fault tolerance. Answer: D Diff: 2 Page Ref: 295 33) In the Big Data and Analytics in Politics case study, which of the following was an input to the analytic system? A) census data B) assessment of sentiment C) voter mobilization D) group clustering Answer: A Diff: 2 Page Ref: 301 34) In the Big Data and Analytics in Politics case study, what was the analytic system output or goal? A) census data B) assessment of sentiment C) voter mobilization D) group clustering Answer: C Diff: 2 Page Ref: 301 35) Traditional data warehouses have not been able to keep up with A) the evolution of the SQL language. B) the variety and complexity of data. C) expert systems that run on them. D) OLAP. Answer: B Diff: 2 Page Ref: 303 5 5/13 36) Under which of the following requirements would it be more appropriate to use Hadoop over a data warehouse? A) ANSI 2003 SQL compliance is required B) online archives alternative to tape C) unrestricted, ungoverned sandbox explorations D) analysis of provisional data Answer: C Diff: 2 Page Ref: 306 37) What is Big Data's relationship to the cloud? A) Hadoop cannot be deployed effectively in the cloud just yet. B) Amazon and Google have working Hadoop cloud offerings. C) IBM's homegrown Hadoop platform is the only option. D) Only MapReduce works in the cloud; Hadoop does not. Answer: B Diff: 2 Page Ref: 308 38) Companies with the largest revenues from Big Data tend to be A) the largest computer and IT services firms. B) small computer and IT services firms. C) pure open source Big Data firms. D) non-U.S. Big Data firms. Answer: A Diff: 2 Page Ref: 311 39) In the health sciences, the largest potential source of Big Data comes from A) accounting systems. B) human resources. C) patient monitoring. D) research administration. Answer: C Diff: 2 Page Ref: 320 40) In the Discovery Health insurance case study, the analytics application used available data to help the company do all of the following EXCEPT A) predict customer health. B) detect fraud. C) lower costs for members. D) open its own pharmacy. Answer: D Diff: 2 Page Ref: 323-324 41) Most Big Data is generated automatically by ________. Answer: machines Diff: 2 Page Ref: 279 6 6/13 42) ________ refers to the conformity to facts: accuracy, quality, truthfulness, or trustworthiness of the data. Answer: Veracity Diff: 2 Page Ref: 282 43) In-motion ________ is often overlooked today in the world of BI and Big Data. Answer: analytics Diff: 2 Page Ref: 282 44) The ________ of Big Data is its potential to contain more useful patterns and interesting anomalies than "small" data. Answer: value proposition Diff: 2 Page Ref: 282 45) As the size and the complexity of analytical systems increase, the need for more ________ analytical systems is also increasing to obtain the best performance. Answer: efficient Diff: 2 Page Ref: 286 46) ________ speeds time to insights and enables better data governance by performing data integration and analytic functions inside the database. Answer: In-database analytics Diff: 2 Page Ref: 286 47) ________ bring together hardware and software in a physical unit that is not only fast but also scalable on an as-needed basis. Answer: Appliances Diff: 2 Page Ref: 286 48) Big Data employs ________ processing techniques and nonrelational data storage capabilities in order to process unstructured and semistructured data. Answer: parallel Diff: 2 Page Ref: 289 49) In the world of Big Data, ________ aids organizations in processing and analyzing large volumes of multi-structured data. Examples include indexing and search, graph analysis, etc. Answer: MapReduce Diff: 2 Page Ref: 291 50) The ________ Node in a Hadoop cluster provides client information on where in the cluster particular data is stored and if any nodes fail. Answer: Name Diff: 2 Page Ref: 292 7 7/13 51) A job ________ is a node in a Hadoop cluster that initiates and coordinates MapReduce jobs, or the processing of the data. Answer: tracker Diff: 2 Page Ref: 292 52) HBase is a nonrelational ________ that allows for low-latency, quick lookups in Hadoop. Answer: database Diff: 2 Page Ref: 293 53) Hadoop is primarily a(n) ________ file system and lacks capabilities we'd associate with a DBMS, such as indexing, random access to data, and support for SQL. Answer: distributed Diff: 2 Page Ref: 294 54) HBase, Cassandra, MongoDB, and Accumulo are examples of ________ databases. Answer: NoSQL Diff: 2 Page Ref: 295 55) In the eBay use case study, load ________ helped the company meet its Big Data needs with the extremely fast data handling and application availability requirements. Answer: balancing Diff: 2 Page Ref: 296 56) As volumes of Big Data arrive from multiple sources such as sensors, machines, social media, and clickstream interactions, the first step is to ________ all the data reliably and cost effectively. Answer: capture Diff: 2 Page Ref: 303 57) In open-source databases, the most important performance enhancement to date is the costbased ________. Answer: optimizer Diff: 2 Page Ref: 304 58) Data ________ or pulling of data from multiple subject areas and numerous applications into one repository is the raison d'être for data warehouses. Answer: integration Diff: 2 Page Ref: 305 59) In the energy industry, ________ grids are one of the most impactful applications of stream analytics. Answer: smart Diff: 2 Page Ref: 315 8 8/13 60) In the U.S. telecommunications company case study, the use of analytics via dashboards has helped to improve the effectiveness of the company's ________ assessments and to make their systems more secure. Answer: threat Diff: 2 Page Ref: 319 61) In the opening vignette, what is the source of the Big Data collected at the European Organization for Nuclear Research or CERN? Answer: Forty million times per second, particles collide within the LHC, each collision generating particles that often decay in complex ways into even more particles. Precise electronic circuits all around LHC record the passage of each particle via a detector as a series of electronic signals, and send the data to the CERN Data Centre (DC) for recording and digital reconstruction. The digitized summary of data is recorded as a "collision event." 15 petabytes or so of digitized summary data produced annually and this is processed by physicists to determine if the collisions have thrown up any interesting physics. Diff: 2 Page Ref: 276 62) List and describe the three main "V"s that characterize Big Data. Answer: ∙ Volume: This is obviously the most common trait of Big Data. Many factors contributed to the exponential increase in data volume, such as transaction-based data stored through the years, text data constantly streaming in from social media, increasing amounts of sensor data being collected, automatically generated RFID and GPS data, and so forth. ∙ Variety: Data today comes in all types of formats–ranging from traditional databases to hierarchical data stores created by the end users and OLAP systems, to text documents, e-mail, XML, meter-collected, sensor-captured data, to video, audio, and stock ticker data. By some estimates, 80 to 85 percent of all organizations' data is in some sort of unstructured or semistructured format. ∙ Velocity: This refers to both how fast data is being produced and how fast the data must be processed (i.e., captured, stored, and analyzed) to meet the need or demand. RFID tags, automated sensors, GPS devices, and smart meters are driving an increasing need to deal with torrents of data in near—real time. Diff: 2 Page Ref: 280-281 9 9/13 63) List and describe four of the most critical success factors for Big Data analytics. Answer: ∙ A clear business need (alignment with the vision and the strategy). Business investments ought to be made for the good of the business, not for the sake of mere technology advancements. Therefore the main driver for Big Data analytics should be the needs of the business at any level–strategic, tactical, and operations. ∙ Strong, committed sponsorship (executive champion). It is a well-known fact that if you don't have strong, committed executive sponsorship, it is difficult (if not impossible) to succeed. If the scope is a single or a few analytical applications, the sponsorship can be at the departmental level. However, if the target is enterprise-wide organizational transformation, which is often the case for Big Data initiatives, sponsorship needs to be at the highest levels and organization-wide. ∙ Alignment between the business and IT strategy. It is essential to make sure that the analytics work is always supporting the business strategy, and not other way around. Analytics should play the enabling role in successful execution of the business strategy. ∙ A fact-based decision making culture. In a fact-based decision-making culture, the numbers rather than intuition, gut feeling, or supposition drive decision making. There is also a culture of experimentation to see what works and doesn't. To create a fact-based decision-making culture, senior management needs to do the following: recognize that some people can't or won't adjust; be a vocal supporter; stress that outdated methods must be discontinued; ask to see what analytics went into decisions; link incentives and compensation to desired behaviors. ∙ A strong data infrastructure. Data warehouses have provided the data infrastructure for analytics. This infrastructure is changing and being enhanced in the Big Data era with new technologies. Success requires marrying the old with the new for a holistic infrastructure that works synergistically. Diff: 2 Page Ref: 285-286 10 10/13 64) When considering Big Data projects and architecture, list and describe five challenges designers should be mindful of in order to make the journey to analytics competency less stressful. Answer: ∙ Data volume: The ability to capture, store, and process the huge volume of data at an acceptable speed so that the latest information is available to decision makers when they need it. ∙ Data integration: The ability to combine data that is not similar in structure or source and to do so quickly and at reasonable cost. ∙ Processing capabilities: The ability to process the data quickly, as it is captured. The traditional way of collecting and then processing the data may not work. In many situations data needs to be analyzed as soon as it is captured to leverage the most value. ∙ Data governance: The ability to keep up with the security, privacy, ownership, and quality issues of Big Data. As the volume, variety (format and source), and velocity of data change, so should the capabilities of governance practices. ∙ Skills availability: Big Data is being harnessed with new tools and is being looked at in different ways. There is a shortage of data scientists with the skills to do the job. ∙ Solution cost: Since Big Data has opened up a world of possible business improvements, there is a great deal of experimentation and discovery taking place to determine the patterns that matter and the insights that turn to value. To ensure a positive ROI on a Big Data project, therefore, it is crucial to reduce the cost of the solutions used to find that value. Diff: 3 Page Ref: 286-287 65) Define MapReduce. Answer: As described by Dean and Ghemawat (2004), "MapReduce is a programming model and an associated implementation for processing and generating large data sets. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system." Diff: 2 Page Ref: 289-290 66) What is NoSQL as used for Big Data? Describe its major downsides. Answer: ∙ NoSQL is a new style of database that has emerged to, like Hadoop, process large volumes of multi-structured data. However, whereas Hadoop is adept at supporting large-scale, batch-style historical analysis, NoSQL databases are aimed, for the most part (though there are some important exceptions), at serving up discrete data stored among large volumes of multistructured data to end-user and automated Big Data applications. This capability is sorely lacking from relational database technology, which simply can't maintain needed application performance levels at Big Data scale. ∙ The downside of most NoSQL databases today is that they trade ACID (atomicity, consistency, isolation, durability) compliance for performance and scalability. Many also lack mature management and monitoring tools. Diff: 2 Page Ref: 295 11 11/13 67) What is a data scientist and what does the job involve? Answer: A data scientist is a role or a job frequently associated with Big Data or data science. In a very short time it has become one of the most sought-out roles in the marketplace. Currently, data scientists' most basic, current skill is the ability to write code (in the latest Big Data languages and platforms). A more enduring skill will be the need for data scientists to communicate in a language that all their stakeholders understand–and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or–ideally–both. Data scientists use a combination of their business and technical skills to investigate Big Data looking for ways to improve current business analytics practices (from descriptive to predictive and prescriptive) and hence to improve decisions for new business opportunities. Diff: 2 Page Ref: 297-298 68) Why are some portions of tape backup workloads being redirected to Hadoop clusters today? Answer: ∙ First, while it may appear inexpensive to store data on tape, the true cost comes with the difficulty of retrieval. Not only is the data stored offline, requiring hours if not days to restore, but tape cartridges themselves are also prone to degradation over time, making data loss a reality and forcing companies to factor in those costs. To make matters worse, tape formats change every couple of years, requiring organizations to either perform massive data migrations to the newest tape format or risk the inability to restore data from obsolete tapes. ∙ Second, it has been shown that there is value in keeping historical data online and accessible. As in the clickstream example, keeping raw data on a spinning disk for a longer duration makes it easy for companies to revisit data when the context changes and new constraints need to be applied. Searching thousands of disks with Hadoop is dramatically faster and easier than spinning through hundreds of magnetic tapes. Additionally, as disk densities continue to double every 18 months, it becomes economically feasible for organizations to hold many years' worth of raw or refined data in HDFS. Diff: 2 Page Ref: 304 69) What are the differences between stream analytics and perpetual analytics? When would you use one or the other? Answer: ∙ In many cases they are used synonymously. However, in the context of intelligent systems, there is a difference. Streaming analytics involves applying transaction-level logic to real-time observations. The rules applied to these observations take into account previous observations as long as they occurred in the prescribed window; these windows have some arbitrary size (e.g., last 5 seconds, last 10,000 observations, etc.). Perpetual analytics, on the other hand, evaluates every incoming observation against all prior observations, where there is no window size. Recognizing how the new observation relates to all prior observations enables the discovery of real-time insight. ∙ When transactional volumes are high and the time-to-decision is too short, favoring nonpersistence and small window sizes, this translates into using streaming analytics. However, when the mission is critical and transaction volumes can be managed in real time, then perpetual analytics is a better answer. Diff: 2 Page Ref: 315-316 12 12/13 70) Describe data stream mining and how it is used. Answer: Data stream mining, as an enabling technology for stream analytics, is the process of extracting novel patterns and knowledge structures from continuous, rapid data records. A data stream is a continuous flow of ordered sequence of instances that in many applications of data stream mining can be read/processed only once or a small number of times using limited computing and storage capabilities. Examples of data streams include sensor data, computer network traffic, phone conversations, ATM transactions, web searches, and financial data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Diff: 2 Page Ref: 317 13 13/13