Study of Hadoop Features for Large Scale Data
Dipali Salunkhe1, Devendra Bahirat2, Neha V. Koushik3, Deepali Javale4
1Dipali Salunkhe, Department of Computer Engineering, MIT College of Engineering, Pune (Maharashtra), India.
2Devendra Bahirat, Department of Computer Engineering, MIT College of Engineering, Pune (Maharashtra), India.
3Neha V. Koushik, Department of Computer Engineering, MIT College of Engineering, Pune (Maharashtra), India.
4Deepali Javale, Department of Computer Engineering, MIT College of Engineering, Pune (Maharashtra), India.
Manuscript received on 12 November 2014 | Revised Manuscript received on 22 November 2014 | Manuscript Published on 30 November 2014 | PP: 18-21 | Volume-4 Issue-6, November 2014 | Retrieval Number: F1849114614/14©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: The data from hospitals around the area (city, state or country) is huge. Handling it through traditional RDBMS will be inefficient and cumbersome because the data from various hospitals won’t be in the same format. Another reason is RDBMS doesn’t offer an efficient way to handle unstructured data (i.e. Media files).Thirdly, as the data becomes voluminous the time for retrieval increases exponentially. Hadoop has many advantages if used to store all the medical data of the patient and also media files related to it (i.e. X-Ray reports, sonography reports and videos of operation). This paper gives overview of Hadoop and its components and also comparison between Hadoop and RDBMS.
Keywords: HDFS, Mapreduce, Hbase
Scope of the Article: Data Mining