Impact of Big Data to Analyze Stock Exchange Data Using Apache Pig
Yogesh Kumar Gupta1, Shruti Sharma2
1Yogesh Kumar Gupta, Faculty of Mathematics and Computing, Banasthali Vidyapith, Jaipur (Rajasthan), India.
2Shruti Sharma, Faculty of Mathematics and Computing, Banasthali Vidyapith, Jaipur (Rajasthan), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 1428-1433 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5594058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Big Data is generated in different formats with high velocity and volume, for those reasons conventional approaches cannot deal with this kind of data. Hence big data tools i.e. Hadoop came into the glare due to its high scalability, availability and cluster environment mechanism accelerate the work in distributed manner. One of the prominent components of Hadoop is MapReduce which can handle any format of data but to use this module highly programming skill is needed for these reason users move towards Apache pig, as we can analyze data only by executing queries. Due to the fault tolerant feature, Pig conceives value under Hadoop platform. In this paper, we analyze stock exchange data of various sectors with the help of Apache pig. Stock exchange data from 2013-2018 is taken and calculate 52 week low, average and high prices through PigLatin script and represented the result in a pictorial form. We also characterize the comparative study of diverse sectors named Banking, IT, Oil and Sugar in order to make the clear image to understand the data. The organized system facilitates the researchers or analysts to come across absolute decision toward spend their assets by means of yield within small instance.
Keyword: Big Data, Hadoop, Apache Pig, Stock Exchange Data, Stock Sectors.
Scope of the Article: Big Data Analytics Application Systems.