School of Science ITECH 2201 Cloud Computing Week 6 Workbook

This document provides a comprehensive solution to the ITECH 2201 Cloud Computing Week 6 workbook, focusing on Big Data concepts. The assignment covers various aspects of data science, including its definition and the percentage of data created in recent years. It delves into the characteristics of Big Data, exploring the 7 V's (Volume, Velocity, Variety, Veracity, Variability, Visualization, and Value) and their significance. The solution also examines Big Data platforms, detailing how to acquire, organize, and analyze data. Furthermore, it discusses Big Data products like Google's PageRank and Facebook's data usage, as well as Big Data tools such as NoSQL databases, MapReduce, and Amazon S3. The assignment concludes with an exploration of Big Data applications across different industries, including nonprofits, sales, and insurance, justifying the benefits of leveraging Big Data in each sector. The solution also touches upon memory virtualization and provides relevant video references for further understanding.

ITECH 2201 Cloud Computing
School of Science, Information Technology & Engineering
Workbook for Week 6 (Big Data)
Please note: All the efforts were taken to ensure the given web links are accessible. However,
if they are broken – please use any appropriate video/article and refer them in your answer
Part A (4 Marks)
Exercise 1: Data Science(1 mark)
Read the article at http://datascience.berkeley.edu/about/what-is-data-science/ and
answer the following:
What is Data Science?
Data science: Multidisciplinary integration of the data reasoning, algorithm development as well as technology
to resolve difficult problems of analysis (Kowolenko & Vouk, 2018).
According to IBM estimation, what is the percent of the data in the world today that has been
created in the past two years?
90% of data in the whole world created in last two years
What is the value of petabytestorage?
Petabyte= 1015 bytes of data
For each course, both foundation and advanced, you find at
http://datascience.berkeley.edu/academics/curriculum/briefly state (in 2 to 3 lines) what
they offer?Based on the given course description as well as from the video. The purpose
of this question is to understand the different streams available in Data Science.
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Foundation: The Master of Information and Data Science curriculum design options can accelerate the
completion of the course. Students who are unfamiliar with object-oriented based programming will need to
complete the Python for the Data Science Foundation classes or course as a main part of their basic course
units.
Advanced: Data science roles and responsibilities are different as well as skills needed for them differ
considerably. The advanced course plays an important role in profound or deep understanding and value as
well as application of the data science.
Exercise 2: Characteristics of Big Data(2 marks)
Read the following research paper from IEEE Xplore Digital Library
Ali-ud-din Khan, M.; Uddin, M.F.; Gupta, N., "Seven V's of Big Data understanding Big
Data to extract value," American Society for Engineering Education (ASEE Zone 1), 2014
Zone 1 Conference of the , pp.1,5, 3-5 April 2014
and answer the following questions:
Summarise the motivation of the author (in one paragraph)
In this article motivation for writing this article as well as outlining pertinent arguments comes from a fact that
Big data has become part of everybody life, as well as big data hide solutions to several problems in any of
these industries. In fact, Big Data provides the raw material for building the next great machine. The author
supports the fact in which big data will ultimately take over new technology and the Internet world.
What are the 7 v’s mentioned in the paper? Briefly describe each V in one paragraph.
1. Volume: This is the main feature that defines big data as "big."
2. Velocity: This can simply be defined as the speed of change.
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3. Variety: It can be simply defined as having different forms of data sources.
4. Veracity: It refers to the credibility of the data being used..
5. Variability: First, the variability is different from the variety.
6. Visualization: It refers to how data is presented to management for decision making.
7. Value: Values are the last, and it is important to understand that organizations need to obtain some
value after tremendous efforts and resources.
Explore the author’s future work by using the reference [4] in the research paper.
Summarise your understanding how Big Data can improve the healthcare sector in 300
words.
The healthcare industry faces many challenges, in which new diseases are spread to maintain good efficiency.
Large data analysis can help to resolve these health-care challenges. Due to the enormous data available in
the healthcare sector such as health, clean, financial, medical, research and development, administration and
operational data, meaningful insights can be found to improve the functioning of the industry. Healthcare
companies have used large numbers to check the hospital's admission rates and analyze staff performance as
a part of their professional intelligence plan. Healthcare firms can cut out the healthcare costs and provide
good care with predictive analysis. Big data assists to reduce the risk of medicines by improving administrative
performance and financial and helps to reduce reading. Medical insurance is complex and suffers from
controversial and fraudulent claims. Big Data Analysis helps facilitate the efficiency of medical insurance
claims by disclosing the trends and exposing the claims process. Patients get good returns on their insurance
claims, and keepers receive fast money, relying on EMR's adoption is filling data in healthcare, and because
carers need to keep records of historical patients, the number of data will only increase, this is good news for
solution providers selling data storage and large data It is only for analysis That's good news. The trend is
seen in EMR, diagnosis, treatment effectiveness, operational effectiveness, vendor expenses and many
things. There is everything to look for the right use case for data.
Exercise 3: Big Data Platform(1 mark)
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In order to build a big data platform - one has to acquire, organize and analyse the big
data. Go through the following links and answer the questions that follow the links: Check
the videos and change the wordings
− http://www.infochimps.com/infochimps-cloud/how-it-works/
− http://www.youtube.com/watch?v=TfuhuA_uaho
− http://www.youtube.com/watch?v=IC6jVRO2Hq4
− http://www.youtube.com/watch?v=2yf_jrBhz5w
Please note: You are encouraged to watch all the videos in the series from Oracle.
How to acquire big data for enterprises and how it can be used?
In the above video, Big Data now shows a big problem, but there is a simple story behind Hype. For decades,
the company has been making professional decisions based on stored transaction data in relational
databases. Oracle offers a comprehensive and highly integrated product portfolio so that you can create and
manage these different types of data, explore new insights and analyze those using existing data to gain
hidden relationships (AlMahmoud, Damiani, Otrok & Al-Hammadi, 2017).
How to organize and handle the big data?
To help research institutions collect, integrate, organize, and analyze data from a variety of sources, a
comprehensive and comprehensive product portfolio is necessary. Before filtering large data research
platforms in data warehouses, users need to process, filter, and transform large amounts of data.
What are the analyses that can be done using big data?
Infrastructure involved in the analysis of large amounts of data needs to support analytics for example
statistical analysis and data mining in order to store large amounts of the data types in various systems; extent
to intense data volumes; provide fast response times; as well as automate based on an logical models
decision making.
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Part B (4 Marks)
Part B answers should be based on well cited article/videos – name the references used
in your answer.For more information read the guidelines as given in Assignment 1.
Exercise 4: Big Data Products (1 mark)
Google is a master at creating data products. Below are few examples from Google.
Describe the below products and explain how the large scale data is used effectively in
these products.
a. Google’s PageRank
PageRank is a measurement that evaluates the number and quality of webpage, determines the
importance of the page and related authentic scores in 0-10 scale.
b. Google’s Spell Checker
Google's spell check is a very old feature that Google constantly improves. It is necessary to
understand how this works to understand keyword research process and to better understand
reputation / brand management.
c. Google’s Flu Trends
Web service operated by the Google.
d. Google’s Trends
Google Trends are publically web based facility for the Google Inc. that is completely based on the
Google Search and it also shows that certain search-periods are often used in various parts of the
world with search-volume and in different languages.
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Like Google – Facebook and LinkedIn also uses large scale data effectively. How?
As a social platform, websites generate mass data. All data is grouped together to analyse behaviours of users
in order to provide recommendations through social media providers. For example, Facebook will notify things
about neglect, so that users can love their posts and their search terms according to their pages or products
they like.
Exercise 5: Big Data Tools(2 marks)
Briefly explain why a traditional relational database (RDBS) is not effectively used to store
big data?
First of all, size of data has been greatly increased to range of PB level, a PB level = 1,024 TB. RDBMS finds it
as very challenging to manage or handle this huge large data. To solve this problem, RDBMS adds more
centralized processing units and more memory generation to the database management systems. Secondly,
most data comes from semi-structured or non-formatted formats of social media, video, text, email and audio
(Zhao, Zhou, Li & Huang, 2018).
What is NoSQL Database?
It offers transaction manipulation, horizontal scalability, and transactional semantics for easy management and
inspection. NoSQL is a database design methodology that can adapt to various data models including various
values, documents, histograms and graphic formats.
Name and briefly describe at least 5 NoSQL Databases
Cassandra originally developed by Facebook and now became Apache open source software, which is well
suited for social networking of the cloud computing databases.
Lucene: Subproject of Apache Software. It is an open source full-text search engine toolkit.
Oracle NoSQL: Oracle NoSQL Database is Oracle's NoSQL type distributed key database.
H Base: An open source, non-relational distributed database
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Big Table non-relational databases: These RDBMSs are mainly used in large enterprise scenarios (Lerner,
2014).
What is MapReduce and how it works?
Map Reduce jobs usually divide the input dataset into separate blocks that are easily processed by the map
task in a totally parallel manner.
Briefly describe some notable MapReduce products (at least 5)
 Disco Project
 Couchdb
 Apache Hadoop
 Infinispan
 Riak
Amazon’s S3 service lets to store large chunks of data on an online service. List some 5
features for Amazon’s S3 service.
1. Versioning
2. Lifecycle
3. Tagging is a cost allocation classification as AWS billing characteristic to track AWS costs
4. Request Pricing
5. RRS is decreased redundancy storage
Getting the concise, valuable information from a sea of data can be challenging. We need
statistical analysis tool to deal with Big Data. Name and describe some (at least 3)
statistical analysis tools.
 SAS has a very comprehensive system of data access,
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 SPSS Statistics: system is convenient, more complete statistical methods
 EXCEL spreadsheet is powerful spreadsheet software (Lerner, 2014)
Exercise 6: Big Data Application (1 mark)
Name 3 industries that should use Big Data – justify your claim in 250 words for each
industry using proper references.
1. Nonprofits
Even though it might seem strange to jump from the achievement of Fortune in almost five hundred companies
to non-profit firms, the technologies that make for-profit companies flourishing are not automatically mutually
exclusive. One large nonprofit that needs to be considered is Wikipedia. Wikipedia is most significant site in
the whole world. It has more data as compared to other non-profit sites. The potential is staggering, but it can
be done to compensate for its costs including: promoting fundraising hard work, adjusting marketing to
accommodate their population and isolation most likely to be updated Donated users (Dhar, 2014).
2. Sales
For profit-making organizations, large data technology used to receive donations is more effective when the
sale is applicable. Actually, if we are in sales-driven industry, and we do not use large data, we will give
ourselves a lot of pain. The sales experience is not at all how to show the sales knowledge. Like non-profit
organizations, our sales section has a commodity or service which can just be done through financial
transactions and many customers are encouraged.
3. Insurance
Insurance industry which is seen as a most popular industries in the world-data-driven industry, so it is
amazing that some people use large scale shopping solutions to increase their business. Most insurance
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companies are like experts, mineral experts, intermediaries like me, accountants and actuators (Kirkpatrick,
2013).
From your lecture and also based on the below given video link:
https://www.youtube.com/watch?v=_sXkTSiAe-A
Write a paragraph about memory virtualization.
Memory virtualization permits networked servers and distributed servers so that they share a large memory
pool to conquer physical memory limitations, an ordinary bottleneck for software performance.
Watch the below mentioned YouTube link:
https://www.youtube.com/watch?v=wTcxRObq738
Based on the video answer the following questions:
What is RAID 0?
RAID 0, is a method or technique for decomposing files and distributing data across each and every disk
drives in RAID group.
Describe Striping, Mirroring and Parity.
 Stripping: Breaks data stripe into a specific size block (known as "block size") and then blocks it in the
entire red section. This type of data storage can affect performance.
 Mirroring: Storage system in which a single copy of the data is stored on RAID member simultaneously.
This form of data or information placement affects the fault tolerance and performance.
 Parity: There is a storage technique that uses parity stripe as well as checksum mechanism. In Parity
Technologies, a certain parity functions are removed for data validation.
Exercise 2: Storage Design (2 marks)
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Summarize storage repository design based on the following video link:
https://www.youtube.com/watch?v=eVQH7C3nulY
The repository is the rational disk space provided by the file system on physical storage hardware. In case we
create a repository on file system, the file system which already exists, such as an NFS share (Rouse, 2009).
Below YouTube link describes the Intelligent Storage System
https://www.youtube.com/watch?v=raTIRsMi7zk
Based on the watched video answer the following questions:
What is ISS?
Smart Storage System: Feature-rich RAID array and it provides extremely optimized output and input
processing capabilities. Also provides plenty of caching and several performance-enhancing I/O paths.
What are the 4 main components of the ISS?
4 components of ISS:
 Cache
 Front end
 Physical disks
 Back end
How cache works in ISS?
Caches are semiconductor memories that temporarily place data to decrease the time necessary to serve host
input and output requests. Caching improves data storage system execution and performance by separating
mechanical delays related to physical disk that is the lowest component of the intelligent data storage system
(RoHS Guild Compliance 2016).
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Storage Area Network (SAN) and Network Attached Storage (NAS) are widely
used concepts in data storage arena. The following YouTube video links gives
detailed description of these concepts:
− http://www.youtube.com/watch?v=csdJFazj3h0
− http://www.youtube.com/watch?v=vdf6CvGQZrk
− https://www.youtube.com/watch?v=KxdfGcynfJ0
− https://www.youtube.com/watch?v=4RsLUTJ_Qtk
Based on the watched videos answer the following questions:
Describe NAS and SAN briefly using diagrams?
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What are the advantages of SAN over NAS?
Advantages
 Virtualization Support
 Resiliency
 Disaster Recovery
Disadvantages
 Compression
 De-duplication
 Thin Provisioning
What are two common NAS file sharing protocols? How they are different from
each other?
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