Discuss about the Analysis of Big Data Vendors Symposium.
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 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.
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).
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
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).
- Mobile application
- Live voting
- Audience interaction for live events
- Log ingestion
- Web session management
- E – commerce shopping carts (Ni et al. 2016)
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.
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.