Business Intelligence, 3e (Sharda/Delen/Turban)
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.
2) The term “Big Data” is relative as it depends on the size of the using organization.
3) In the Luxottica case study, outsourcing enhanced the ability of the company to gain insights
into their data.
4) Many analytics tools are too complex for the average user, and this is one justification for Big
Data.
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.
6) Big Data uses commodity hardware, which is expensive, specialized hardware that is custom
built for a client or application.
7) MapReduce can be easily understood by skilled programmers due to its procedural nature.
8) Hadoop was designed to handle petabytes and extabytes of data distributed over multiple
nodes in parallel.
9) Hadoop and MapReduce require each other to work.
10) In most cases, Hadoop is used to replace data warehouses.
11) Despite their potential, many current NoSQL tools lack mature management and monitoring
tools.
12) The data scientist is a profession for a field that is still largely being defined.
13) There is a current undersupply of data scientists for the Big Data market.
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.
15) For low latency, interactive reports, a data warehouse is preferable to Hadoop.
16) If you have many flexible programming languages running in parallel, Hadoop is preferable
to a data warehouse.
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.
18) It is important for Big Data and self-service business intelligence go hand in hand to get
maximum value from analytics.
19) Big Data simplifies data governance issues, especially for global firms.
20) Current total storage capacity lags behind the digital information being generated in the
world.
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.
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.
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
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
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
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
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
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.
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
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
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
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.
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
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
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.
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
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.
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.
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.
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.
41) Most Big Data is generated automatically by ________.
42) ________ refers to the conformity to facts: accuracy, quality, truthfulness, or trustworthiness
of the data.
43) In-motion ________ is often overlooked today in the world of BI and Big Data.
44) The ________ of Big Data is its potential to contain more useful patterns and interesting
anomalies than “small” data.
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.
46) ________ speeds time to insights and enables better data governance by performing data
integration and analytic functions inside the database.
47) ________ bring together hardware and software in a physical unit that is not only fast but
also scalable on an as-needed basis.
48) Big Data employs ________ processing techniques and nonrelational data storage
capabilities in order to process unstructured and semistructured data.
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.
50) The ________ Node in a Hadoop cluster provides client information on where in the cluster
particular data is stored and if any nodes fail.
51) A job ________ is a node in a Hadoop cluster that initiates and coordinates MapReduce jobs,
or the processing of the data.
52) HBase is a nonrelational ________ that allows for low-latency, quick lookups in Hadoop.
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.
54) HBase, Cassandra, MongoDB, and Accumulo are examples of ________ databases.
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.
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.
57) In open-source databases, the most important performance enhancement to date is the cost-
based ________.
58) Data ________ or pulling of data from multiple subject areas and numerous applications into
one repository is the raison d’être for data warehouses.
59) In the energy industry, ________ grids are one of the most impactful applications of stream
analytics.
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.
61) In the opening vignette, what is the source of the Big Data collected at the European
Organization for Nuclear Research or CERN?
62) List and describe the three main “V”s that characterize Big Data.
63) List and describe four of the most critical success factors for Big Data analytics.
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.
65) Define MapReduce.
66) What is NoSQL as used for Big Data? Describe its major downsides.
67) What is a data scientist and what does the job involve?
68) Why are some portions of tape backup workloads being redirected to Hadoop clusters today?
69) What are the differences between stream analytics and perpetual analytics? When would you
use one or the other?
70) Describe data stream mining and how it is used.