Identifying Risks in Big Data and Cloud Computing

The assignment content discusses the importance of Big Data and Cloud Computing in today's businesses. It highlights the benefits of using these technologies, including cost-effectiveness, scalability, and accessibility. However, it also mentions some potential risks, such as data privacy concerns and downtime threats. The content provides examples of how companies like Philips and wearable gadgets are leveraging Big Data and Cloud Computing to improve their operations and customer experiences. Additionally, it introduces Hadoop as an open-source project that can help handle the challenges of storing and maintaining large amounts of data. Overall, the assignment emphasizes the need for effective risk analysis and planning when implementing Big Data and Cloud Computing in businesses.

Assignment Title
Assignment Title
Module code
Full name and student code
Submission date

Assignment Title
Summary
In the past few years, Big Data and Cloud Computing have emerged as the game
changers in many fields. However, they pose challenges such as security, down-time,
mismatched analysis, etc. This paper explores the usage of Big Data and Cloud Computing –
What are they, characteristics, advantages, challenges, and how they are associated to each
other. Also, several real time scenarios have been explained as examples.
Independently, Big Data and Cloud Computing are both very impressive phenomena
but together they are formidable. The cloud can make Big Data within reach where as Big
Data provides structured information that increases the success rate. This will be an
interesting trend to watch out for in the future.

Assignment Title
Table of Contents
Summary...............................................................................................................................................2
Introduction...........................................................................................................................................4
Characteristics.......................................................................................................................................5
Big Data.............................................................................................................................................5
Cloud Computing..............................................................................................................................5
Benefits and Challenges........................................................................................................................6
Big Data.............................................................................................................................................6
Analysis and Decision Making......................................................................................................6
Identification of Risks....................................................................................................................6
Data Volume..................................................................................................................................6
Cloud Computing..............................................................................................................................7
Cost effective.................................................................................................................................7
Scalability......................................................................................................................................7
Accessibility..................................................................................................................................7
Usage of Big Data and Cloud Computing in real time...........................................................................8
Example – 1 (Public Relations).........................................................................................................8
Example – 2 (Wearable Gadgets)......................................................................................................8
Example – 3 (Health Care)................................................................................................................9
Hadoop................................................................................................................................................10
Before and after using Cloud Computing............................................................................................11
Before ................................................................................................................................11
After................................................................................................................................................11
Recommendations...............................................................................................................................12

Assignment Title
Introduction
Previously, a large amount of data was stored and analysed on a desktop level where
the transforming of unstructured data into meaningful information was both time and space
consuming and error prone. However, as time progressed, the methods of capturing and
utilizing the data also evolved.
Big Data and Cloud Computing are the new methods with which we can collect,
analyse, structure, and utilize the data to the maximum possible extent.
Big Data is a term used to describe generally large and complex amounts of semi
structured or unstructured collection of data. The Big Data is important to any organisation or
an individual because it gives the chance to analyse the data and use it to get the best possible
results. However, the traditional data processing tools can neither store nor analyse such huge
amounts of data.
Cloud Computing or simply “The Cloud” allows users to store information virtually
over an application or a machine.
Big Data and Cloud Computing together can make the data accessible anywhere. The
dynamic size of the cloud allows you to increase or decrease the space as per the amounts of
data.

Assignment Title
Characteristics
Big Data
The main characteristics of Big Data are as follows:
 Volume – The name “Big Data” is self-explanatory. The size of the data determines
whether a particular data can be classified as Big Data or not.
 Variety – The data input may vary from one source to another as it is gathered from a
variety of resources. Also, the data itself may be inconsistent at times. This hampers
the data analysis there by giving a misguided result.
 Velocity – The data flow or the velocity is immense as a huge amount of data is
always stored continuously.
Cloud Computing
The main characteristics of Cloud Computing are as follows:
 On-Demand Usage – A customer can gather information, compute, store, etc.,
automatically or with minimum human intervention.
 Universal Access – A customer can access through a variety of platforms such as
mobile phones, tablets, laptops, and workstations.
 Multi-Tenancy – Multiple users can use the cloud from various physical and virtual
resources that are dynamically assigned according to the demand.
 Scalability – The ability to grow and shrink based on the data quantity at any given
time with no impact on applications or users.
 Measured Usage – Automatic optimization of resources by controlling the
performance with a pay-as-you-go pricing model.

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Benefits and Challenges
Big Data
Analysis and Decision Making
Anything and everything can be analysed. The information can be automatically fine-
tuned based on the existing prerequisites. However, the data authenticity is sometimes
questionable which might not give the best possible results always. Hence, the end result
might be misleading.
Identification of Risks
Recurring patterns are identified through continuous analysis of data. This helps in
narrowing down any potential risks that may crop up. However, as the data flow is dynamic it
may not be entirely accurate in assuming a scenario as risk.
Data Volume
As mentioned above, huge amounts of data can be stored over the cloud. However,
recalling the most suitable information for analysis is always a challenge.

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Cloud Computing
Cost effective
Annual operating and storage costs are very less when compared to storing them on a
hard drive. However, there are concerns with the safety and privacy of important data stored
remotely as there is possibility of private data intermixing with other data that makes
businesses vulnerable.
Scalability
The storage space is almost unlimited and grows or shrinks as per the requirement
automatically. However, minimum human intervention might lead to more and more automated
results that are in turn misleading.
Accessibility
The stored information can be accessed from anywhere anytime. However, downtime
poses a serious threat. This can be partially overcome by backing up some amount of data on
your desktop but this in turn is both space and time consuming and does not guarantee
genuine results.

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Usage of Big Data and Cloud Computing in real time
Example – 1 (Public Relations)
Philips is one such example that uses Big Data to its advantage in achieving great
results. One instance is that when they first used it in one of their product expos. During the
first year of usage, they tracked simple data like number of visitors, registrations, etc. They
gathered data that on an average a visitor would stay in the booth for about 32 minutes and
visit for about 1.3 times. For the next expo they included an exit survey to the already
existing data. This gave them results that more than 50% of the visitors were highly probable,
and 33% were absolutely positive to purchase one or more of their products within the next
year. They were certainly benefiting from all the data collected. Based on all the data,
statistics, and predictions, a model is formulated and validated to identify visitor specific
behaviour that is most predictive of an eventual purchase. There by optimizing the exhibit
design and staffing and using them to their advantage in analysing and motivating customer
behaviours that are related to eventual purchase of the product.
Example – 2 (Wearable Gadgets)
Smart Watches are another example where data is collected and analysed
automatically. In case of fitness related applications, your everyday fitness routine is
captured; for example, in the form of number of steps or the amount of time that you spent on
an exercise routine. The time during which you start and stop, the duration, the calories burnt,
the type of exercise may vary from day to day. All the input data is streamlined and analysed
accordingly to give you a tailored regime that gives you the best possible results.

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Example – 3 (Health Care)
Sensors are used to monitor a patient’s health regularly. The sensors are connected to
cloud-based technology which allows patients to be monitored round the clock and to have
control on their own health. This allows in predicting near future health patterns there by
being prepared in providing proactive support whenever needed. Instead of going to a doctor
for check-ups, the patient can himself see what’s going on throughout that time.

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Hadoop
Hadoop is an open source project by Apache. It consists of many small modules which belong
to structuring, processing, and distributed computing categories. The Hadoop framework consists of
the following modules:
 Hadoop Common – This module contains libraries and utilities that are required by other
Hadoop modules.
 Hadoop Distributed File System or HDFS – This module is a distributed file-system that
stores large files across multiple number of machines.
 Hadoop YARN – This module manages computing resources in clusters and uses them for
user applications.
 Hadoop MapReduce – This module is used for large scale data processing
There are many problems in storing and maintaining large amounts of data. In recent
times, the storage capacities of the drives have been increased but data reading speed has not.
The reading process still takes a large amount of time and the writing process is still slower.
The reading time can be very well reduced by reading from multiple disks at the same time.
Chances of failure are more when there are multiple pieces of hardware from where
the data is called. Replicating, making more copies of the data and storing them in multiple
devices may solve this problem.
Another challenge would be of combining and structuring the data being read from
different devices.
Hadoop is designed to handle all the above mentioned challenges. HDFS handles
failure prevention whereas Map Reduce handles data structuring.
Thus Hadoop provides overall handling, analysis, and security.

Information is
gathered and
stored on a
cloud.
Information is
analyzed
based on some
key words and
algorithms
Segregation of
information
based on the
prerequisites
Transferring
the
information to
specified
destinations .
Maintananenc
e of consistent
data
Assignment Title
Before and after using Cloud Computing
The following procedures compare the before and after effects of using Big Data and Cloud Computing together in today’s businesses.
Before After
After
Scattered
information
from various
sources
Limited
Analysis of the
vast
information
Haphazard
segregation
Transferring
the
information
to unwanted
sources.
Lost Data
and
incorrect
results

Assignment Title
Recommendations
Creating multiple meaningful parts of all the data being stored is a good practice for
Big Data analytics.
It needs to be divided into several parts and facts. All the parts should then be
associated to specific surrogate keys meaning that these keys are not interchangeable by any
particular rule. They are in turn assigned in sequence or generated by precise algorithms that
ensure uniqueness of the result.
Integrating both structured and unstructured data as per the requirement and analysing
together might reduce time as all kind of data is a part of Big Data.
Doing a risk analysis to identify the possible security threats that may arise and pre-
planning to counter them is one of the best practices for Cloud Computing. Also, identifying
the relevant type of cloud (Public, Private or Hybrid) for planned processing is also
recommended.