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http://localhost:8080/xmlui/handle/123456789/2316Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Zalte, Sagar A. | - |
| dc.contributor.author | Takate, Vishwas R. | - |
| dc.contributor.author | Chaudhari, Saish R. | - |
| dc.date.accessioned | 2019-08-14T12:07:15Z | - |
| dc.date.available | 2019-08-14T12:07:15Z | - |
| dc.date.issued | 2017-02-01 | - |
| dc.identifier.uri | http://192.168.3.232:8080/jspui/handle/123456789/2316 | - |
| dc.description.abstract | The caption for Hadoop is big data analytics. That means perform Analytics over big data. Traditional technologies such as R, SQL, and Vertica cannot deal with big data. Hadoop can store unstructured semi structured and structured data. Two core component of Hadoop are 1.HDFS 2.Map Reduce. The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. Map Reduce is an execution engine of Hadoop, it process the data stored in HDFS in distributed manner. | en_US |
| dc.subject | Hadoop | en_US |
| dc.subject | HDFS | en_US |
| dc.subject | distributed file system | en_US |
| dc.subject | Map-Reduce | en_US |
| dc.title | Study of Distributed File System for Big Data | en_US |
| Appears in Collections: | Study of Distributed File System for Big Data | |
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