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  <title>DSpace Collection:</title>
  <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2315" />
  <subtitle />
  <id>http://localhost:8080/xmlui/handle/123456789/2315</id>
  <updated>2026-06-23T06:34:06Z</updated>
  <dc:date>2026-06-23T06:34:06Z</dc:date>
  <entry>
    <title>Study of Distributed File System for Big Data</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2316" />
    <author>
      <name>Zalte, Sagar A.</name>
    </author>
    <author>
      <name>Takate, Vishwas R.</name>
    </author>
    <author>
      <name>Chaudhari, Saish R.</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/2316</id>
    <updated>2019-08-14T12:07:36Z</updated>
    <published>2017-02-01T00:00:00Z</published>
    <summary type="text">Title: Study of Distributed File System for Big Data
Authors: Zalte, Sagar A.; Takate, Vishwas R.; Chaudhari, Saish R.
Abstract: The caption for Hadoop is big data analytics. That means perform Analytics over big data. Traditional&#xD;
technologies such as R, SQL, and Vertica cannot deal with big data. Hadoop can store unstructured semi structured and&#xD;
structured data. Two core component of Hadoop are 1.HDFS 2.Map Reduce. The Hadoop Distributed File System&#xD;
(HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user&#xD;
applications. Map Reduce is an execution engine of Hadoop, it process the data stored in HDFS in distributed manner.</summary>
    <dc:date>2017-02-01T00:00:00Z</dc:date>
  </entry>
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