Investigating Applicants on Data Analysis via Serverless Tech Report

This report delves into the application of serverless cloud technology for data analysis, focusing on its potential to identify and predict potential applicants. It explores key concepts like big data, serverless technology, and distributions such as Apache Cassandra and AWS EMR. The report outlines the methodology, including the use of agile models and technologies like Spark stacks and Python. It discusses the risks associated with big technology and the importance of data governance (GDPR). The expected outcomes and conclusion highlight the relationship between data analysis and applicant prediction, emphasizing the efficiency of serverless cloud technology in achieving these goals. The report also includes an annotated bibliography and references, providing a comprehensive overview of the topic.

Potential Applicants On Data
Analysis using Server less cloud
technology

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
Aim.........................................................................................................................................1
Objectives...............................................................................................................................1
Research Questions................................................................................................................1
Big Data...........................................................................................................................................1
Server less Technology....................................................................................................................2
Distributions.....................................................................................................................................2
Apache Cassandra..................................................................................................................2
Pros and Cons..................................................................................................................................2
Pros.........................................................................................................................................2
Cons........................................................................................................................................3
Methodology....................................................................................................................................3
Technologies and Language used....................................................................................................3
Risk using big technology................................................................................................................3
Data Governance (GDPR)...............................................................................................................4
Expected Outcomes and Conclusion...............................................................................................4
Annotated Bibliography...................................................................................................................4
REFERENCES................................................................................................................................5

INTRODUCTION
Data analysis, which is also considered as the data analytics is a composed process which
helps in a multi functioning (Eivy, 2017). Various functions that are included in the same are
inspecting, absolving, transforming and a proper modelling of the information and data. The
basic forte of data analytics is to discover an efficient and appropriate set of data. Also, if
something or factor which needs to be modified can be updated and then considering all the
facts, one can reach to a final conclusion. The proposal is about identifying the rate of potential
applicants on data analysis using server less cloud technology
Aim
To understand about investigating potential applicants on data analysis using server less
cloud technology
Objectives
 To analyse the data analytics and investigating potential applicants using server less
cloud technology.
 To determine the relationship between the data analysis and predicting potential
applicants.
 To recommend ways for understanding the impact of server less cloud technology for
predicting the potential applicants.
Research Questions
Q1 What is data analytics and the server less technology associated with it?
Q2 What is the relationship between data analytics and predicting the potential applicants?
Big Data
Big data is considered as the sets of data and information that is enough capable of
maintaining a huge amount of data processing software's and applications that a basic software
cannot do. There are various challenging factors that can be considered and get simpler such as
the overall storage of data, sharing of files and other information between systems, modifications
and maintaining specifiable privacy. In this, it involves all the structure, unstructured and the
semi structured data but the focus here is on the unstructured data (Glikson, Nastic and Dustdar,
2017). Thus, big data can also be classified as bigger sets consisting of a huge amount of
information and data. Also, it can be analysed in a way that it can help in providing efficient and
appropriate patterns that can help in making further operating and functioning easier and simpler.
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Server less Technology
The server less technology can be used in order to identify the potential applicants
because it seems as much efficient and appropriate option for making a better investigation.
Also, it provides an enhanced level of scalability and it considers the fact that things take time.
There are various people who wants to have the fastest app but they should also ensure that
whether it will be able to take that much of load or not. The server less technology also seems
quite less costly, whether it is related to the computing or the human resources, it is cost
effective, thus can prove a better option for identifying the potential applicants on data analytics
(Jonas and Recht, 2017). Therefore, infrastructure of the server less technology can be
considered as decomposition of the application on the end of server side in various different
functions that can further help in performing essential functions.
Distributions
Apache Cassandra
Apache Cassandra is considered as a No SQL database but an open sourced that is
capable of managing huge amount of information and data by enormous commodity servers.
There are multiple number of data centres and it provides an efficient amount of support to these
data centres as it helps in providing them low latency operations for every client of their (Chang
and Fink, 2017).
AWS EMR
This can also be classified as an efficient option for distribution of data and this option
has other benefits as well. EMR is a well and cost effective option that can prove every
successful in identifying the potential applicants on data analytics. It has been observed that
every machine of EMR costs for about $2,453 which seems a much effective option for a cost
effective factor. Also, it is cheaper than EC2 cluster as well.
Pros and Cons
While doing the data analysis using server less cloud technology, it is obvious enough
that there must be some complications as well along with the benefits. Both of these are
discussed as following :
Pros
 Server less cloud technology seems a better option for an efficient management of all the
resources of server.
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 It is cost efficient for the long run as well.
Cons
 There might be the possibility that some issues may occur in the performance of the
computing (Lynn, Rosati and Emeakaroha, 2017).
 Debugging of the server less cloud technology is quite tricky, if the trick works, it can
resolve the issue and if it does not, it can make it more complicated and will be requiring
then a technical support.
Methodology
In the methodology section, agile model can act quite effective as it is considered as an
umbrella phase which means covering a wide number of areas and processes inside it. There are
various agile methodologies that can be used for analysing the effectiveness of the server less
cloud technology and some very well known and common examples include extreme
programming (XP), crystal, lean development, dynamic system's development method (DSDM)
etc. that can help in analysing the potential applicants on the data analysis using the server less
technology. Thus, they all can be used in order to achieve the targets in a faster amount of time.
Technologies and Language used
For a proper and well demonstration, the whole process can be represented by means of
use cases as well. Use case diagrams are considered as a methodology that helps in providing a
clear view of all the requirements of the system. It also provides a well representation of all the
possible interactions. Also, spark stacks can be used in this research as it is a well maintained
computing system that allows all the components of higher levels as well. So, while doing the
data analysis with the help of server less cloud technology, use of spark stacks can actually help
in achieving all the objectives at a much faster rate. Also, the whole programming will be
accomplished in Python language.
Risk using big technology
Using any big technology, as there are more chances of achievements but there are some
risks as well. The major risk is the maintenance of the technology because as it is being involved
in the company, it seems quite hard for the people to make use of it (Boza and Plaza, 2017). So,
this can cause major complications as well, which is considered as one of the major risk. Also,
Managing proper security and safety by means of the technology is a tough task as it is obvious
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enough that people will take time to get aware of all the ways and methods of processing the
same.
Data Governance (GDPR)
GDPR refers to the general data protection regulation, helps in providing ways for
strengthening all the provisions of data and information. It has also been observed that the
foundation of the compliance of GDPR is considered as the data governance. There are various
other services associated as well that can help in achieving all the targets and objectives at much
faster rate.
Expected Outcomes and Conclusion
Data analytics is a process that is composed of various other factors such as inspecting,
cleansing, framing of data and transforming. There are various tools of the data analysis that can
be used in order to investigate about various methods that can help in predicting the rate of
potential applicants. So, it can be considered that there is a very huge relationship between the
data analysis and prediction of potential applicants. Therefore, it can be concluded that the
process of data analysis and the tools associated with it are capable enough of helping in
investigating the overall rate of the potential applicants.
It can be concluded from the research that there are various types of tools that are
associated with the data analysis and out of all, any tool or method can be used so that it can help
in a better prediction of the potential applications. All the aspects of the research methodology
have been included in order to make the research better and appropriate.
Annotated Bibliography
In this research, it has been analysed by different authors that using the server less cloud
computing technology, investigating of potential applicants can be termed useful enough in
achieving all the objectives efficiently. Agile model methodology has been used in order to cover
all the essential aspects of the research methodology. Various distributions such as Apache
Cassandra and AWS EMR has been used as well because they are considered as an efficient set
of methods for producing the data and information. Different tools for analysing the data has also
been used for an efficient production of the potential applicants.
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REFERENCES
Books and Journals
Eivy, A., 2017. Be Wary of the Economics of" Serverless" Cloud Computing. IEEE Cloud
Computing. 4(2). pp.6-12.
Boza, E. F.. and Plaza, J. A., 2017, October. Reserved, on demand or serverless: Model-based
simulations for cloud budget planning. In Ecuador Technical Chapters Meeting (ETCM),
2017 IEEE (pp. 1-6). IEEE.
Lynn, T., Rosati, P. and Emeakaroha, V., 2017, December. A Preliminary Review of Enterprise
Serverless Cloud Computing (Function-as-a-Service) Platforms. In 2017 IEEE
International Conference on Cloud Computing Technology and Science (CloudCom)(pp.
162-169). IEEE.
Glikson, A., Nastic, S. and Dustdar, S., 2017, May. Deviceless edge computing: extending
serverless computing to the edge of the network. In Proceedings of the 10th ACM
International Systems and Storage Conference (p. 28). ACM.
Chang, K. S. P. and Fink, S. J., 2017, October. Visualizing serverless cloud application logs for
program understanding. In Visual Languages and Human-Centric Computing (VL/HCC),
2017 IEEE Symposium on (pp. 261-265). IEEE.
Jonas, E. and Recht, B., 2017, September. Occupy the cloud: distributed computing for the 99%.
In Proceedings of the 2017 Symposium on Cloud Computing (pp. 445-451). ACM.
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