Analyzing the Impact of AWS on Big Data Analytics Use Cases

The report delves into the transformative role of Amazon Web Services (AWS) in big data analytics. It explores how AWS facilitates large-scale data processing with tools like Lambda, Kinesis, EMR, and Redshift, offering scalability, flexibility, and high performance. The analysis covers common use cases such as mobile applications, gaming, and e-commerce. AWS's capability to dynamically adjust computing resources makes it an optimal choice for big data solutions.

Running head: AMAZON WEB SERVICE USE CASE
AMAZON WEB SERVICE USE CASE
Name of the Student
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Author Note

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Table of Contents
Introduction................................................................................................................................3
The AWS Advantage in the Big Data Analytics........................................................................3
AWS Services............................................................................................................................3
Services......................................................................................................................................3
Framework.................................................................................................................................4
Common use cases.....................................................................................................................4
Conclusion..................................................................................................................................4
References..................................................................................................................................5

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Introduction
The amount of data which is generated and collected in recent times can be
considered to enormous and continuously increasing. The concept of analysing the data is
becoming a challenging part with the analytical tools which are traditional. The need of
innovation and to narrow the gap which exist is very much becoming crucial. The big data
technology and the tool offers the challenges and the opportunity in directly resolving the
problem. The concept of analysing the data is done very much efficiently basically to
increase the customer understanding, gain a competitive advantage in the market place and
grow the respective business areas (Erl, Khattak and Buhler 2016).
The main aim of the report is to put emphasis on the concept of the big data relating to
the AWS. The justification for the selection of the terminology as a main use case is taken
into account, and how the concept would be beneficial is taken into consideration.
The AWS Advantage in the Big Data Analytics
Analysing the large data set basically require significant capacity to compute that
basically depend upon the size of the data being processed. The type of analysis which is to
be generated on the other hand can stand a crucial point in this area of concern. The
characteristics which is related to the big data workload is ideally suited for up and down
which is basically on demand. Changes are very much evitable in each and every phase in
such a situation it can be easily done by resizing the environment which can be done
vertically or horizontally. This is done without the basic need of additional hardware or the
requirement of additional investment (Neves and Bernardino 2017).
Taking into consideration applications which are mission critical, the system
designers have no other choice but to directly over position. The system would be responsible
for the factor of the surge of the additional data. On contrast, on the AWS the provision of the

4AMAZON WEB SERVICE USE CASE
compute and the capacity can be done in a matter of minutes (Sharma 2016). This directly
means that the application of the big data grows and shrink as the demand and the system
runs as close to the efficiency which is optimal.
AWS Services
The AWS has many options to help the data get into the infrastructure of the cloud.
This mainly include devices which are secured such as AWS Import/Export snowball which
is for accelerating the petabyte – scale data transfer, Amazon kinases firehose to mainly load
the streaming data and the scalable private connections through the implementation of AWS
direct connect (Zhu 2016). As the concept of the mobile continue to increase with prospective
to usage, the suit can be used within the AWS Mobile hub to measure application usage and
to collect or export that data to another service for the future analysis (Neves and Bernardino
2017).
Services
These services are basically described with the concept of processing, collecting,
storing and analysing the big data.
 AWS Lambda
 Amazon kinesis Stream
 Amazon elastic map reduce
 Amazon dynamo DB
 Amazon redshift
 Amazon redshift
 Amazon elastic search service
 Amazon quick sight

5AMAZON WEB SERVICE USE CASE
Framework
Amazon EMR is a highly distributed framework of computing to process and store
the data quickly in a manner which is cost effective. Amazon EMR uses Apache Hadoop
which is an open source framework. This is to distribute and process the data across a
resizable cluster of the amazon EC2. On the other it gives the facility to use the most
common tools such as Pig, Hive, Spark etc. Hadoop provides a basic framework to run big
data analytics and the process, it does all the heavy lifting which is involved in the process of
managing, provisioning and maintaining the software and infrastructure of a cluster Hadoop
(Da Cunha Rodrigues et al 2016).
Common use cases
 Mobile application
 Gaming
 Live voting
 Audience interaction for live events
 Log ingestion
 Web session management
 E – commerce shopping carts (Ni et al. 2016)
Conclusion
As more and more data is collected and generated data analysis requires the concept
of flexibility, scalability and high performance tools to provide basic insight in a timely
manner. However, organisations are facing a big data ecosystem growth where new tools
emerged and “die” quickly. Therefore, it can be stated that it can be very much difficult to
keep with the pace and choose the tools which are right. The AWS provides many solutions
to basically address the concept of the big data analytic requirement.

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References
Da Cunha Rodrigues, G., Calheiros, R.N., Guimaraes, V.T., Santos, G.L.D., De Carvalho,
M.B., Granville, L.Z., Tarouco, L.M.R. and Buyya, R., 2016, April. Monitoring of cloud
computing environments: concepts, solutions, trends, and future directions. In Proceedings of
the 31st Annual ACM Symposium on Applied Computing (pp. 378-383). ACM.
Erl, T., Khattak, W. and Buhler, P., 2016. Big data fundamentals: concepts, drivers &
techniques. Prentice Hall Press.
Neves, P.C. and Bernardino, J., 2017. Analysis of Big Data vendors for SMEs. International
Journal of Business Information Systems, 25(4), pp.456-473.
Ni, L.M.S., Xiao, J. and Tan, H., 2016. The golden age for popularizing big data. Science
China Information Sciences, 59(10), p.108101.
Sharma, S., 2016. Expanded cloud plumes hiding Big Data ecosystem. Future Generation
Computer Systems, 59, pp.63-92.
Zhu, H., 2016. Distributed Cloud YunFS: Concepts and Design.