Map Reduce, Pig and Hive on Climatic Condition
K. Govinda1, A. Shubham Nair2, Somula Ramasubbareddy3
1K. Govinda, SCOPE,VIT Univeristy, Vellore, Tamilnadu India.
2A. Shubham Nair, SCOPE,VIT Univeristy, Vellore, Tamilnadu India.
3Somula Ramasubbareddy, Information Technology, VNRVJIET, Hyderabad, Telangana India
Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1144-1148 | Volume-8 Issue-11S September 2019 | Retrieval Number: K123109811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1231.09811S19
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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: This paper addresses recent growth within thes caleand kind of Earth science knowledge has provided new opportunities to massive knowledge analytics analysis for understanding the Earth’s physical processes. There has been associate upsurge of natural science datasets within the past few decades that are being frequently collected mistreatment various modes of acquisition, at deferent scales of observation, and in numerous knowledge sorts and formats. Earth science knowledge sets but exhibit some distinctive characteristics (e.g. adherence to physical properties and spatiotemporal constraints), that gift challengestoancientdata-centric approaches.In this paper the comparative study of Hadoop’s programming paradigm (Map reduce)andHadoop’s ecosystems Hive and Pig.The processing time of map reduce, hive and pig is implement edona data set with simple queries. It is observed that Map reduce processes the datain shorter time ascompared with Map reduceand Hive. It is not necessary that only Map Reduce is useful other techniques are also useful under differentconstraints.
Keywords: Data Pre-processing, Hadoop, Sqoop, Pig, Hive, Map reduce.
Scope of the Article: Data Analytics