Research Journal of Science and Technology

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Big Data Technologies:A Case Study


Affiliations
1 SPMVV Department of Computer Science, SPMVV, Tirupathi, India
     

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The development of Big Data is rapidly accelerating and affecting all areas of technologies by increasing the benefits for individuals and organizations. Big data can be categorized by its volume, variety and velocity. Since data size is bigger, it requires sophisticated techniques, tool and architectures to analyze the data. To extract knowledge from Big Data, various models, programs, softwares, hardwares and technologies have been designed and proposed. They try to ensure more accurate and reliable results for Big Data applications. In fact, many parameters should be considered: technological compatibility, deployment complexity, cost, efficiency, performance, reliability, support and security risks. This paper is a case study that review recent technologies developed for Big Data. It aims to help to select and adopt the right combination of different Big Data technologies according to their technological needs and specific applications’ requirements.

Keywords

Big Data, Deployment Complexity, Volume, Variety, Velocity.
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  • Acharjya, D., Ahmed, K.P., 2016a. A survey on big data analytics: challenges, open research issues and tools. Int. J. Adv. Comput. Sci. App. 7, 511–518.

  • Aher, S.B., Kulkarni, A.R., 2015. Hadoop mapreduce: a programming model for large scale data processing. Am. J. Comput. Sci. Eng. Surv. (AJCSES) 3, 01–10.

  • Ali, A., Qadir, J., urRasool, R., urRasool, R., Sathiaseelan, A., Zwitter, A., Crowcroft, J., 2016. Big data for development: applications and techniques. Big Data Anal. 1, 2.

  • Ames, A., Abbey, R., Thompson, W., 2013. Big Data Analytics Benchmarking SAS, R, and Mahout. SAS Technical Paper.

  • Azarmi, B., 2016a. The big (data) problem. In: Scalable Big Data Architecture. Springer, pp. 1–16.

  • Azarmi, B., 2016b. Scalable Big Data Architecture. Springer.

  • Bansal, H., Mehrotra, S., Chauhan, S., 2016. Apache Hive Cookbook. Packt Publ. Benjelloun, F.-Z., Ait Lahcen, A., 2015. Big data security: challenges, recommendations and solutions. In: Handbook of Research on Security Considerations in Cloud Computing. IGI Global, pp. 301–313.

  • Benjelloun, F.-Z., Ait Lahcen, A., Belfkih, S., 2015. An overview of big data opportunities, applications and tools. In: Intelligent Systems and Computer Vision (ISCV), 2015 (pp. 1–6). IEEE.


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  • Big Data Technologies:A Case Study

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Authors

Padmavathi Vanka
SPMVV Department of Computer Science, SPMVV, Tirupathi, India
T. Sudha
SPMVV Department of Computer Science, SPMVV, Tirupathi, India

Abstract


The development of Big Data is rapidly accelerating and affecting all areas of technologies by increasing the benefits for individuals and organizations. Big data can be categorized by its volume, variety and velocity. Since data size is bigger, it requires sophisticated techniques, tool and architectures to analyze the data. To extract knowledge from Big Data, various models, programs, softwares, hardwares and technologies have been designed and proposed. They try to ensure more accurate and reliable results for Big Data applications. In fact, many parameters should be considered: technological compatibility, deployment complexity, cost, efficiency, performance, reliability, support and security risks. This paper is a case study that review recent technologies developed for Big Data. It aims to help to select and adopt the right combination of different Big Data technologies according to their technological needs and specific applications’ requirements.

Keywords


Big Data, Deployment Complexity, Volume, Variety, Velocity.

References