Hadoop 1.x Limitations

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Hadoop 1.x has many limitations or drawbacks. Main drawback of Hadoop 1.x is that MapReduce Component in it’s Architecture. That means it supports only MapReduce-based Batch/Data Processing Applications. Hadoop 1.x has the following Limitations/Drawbacks: Only one NameNode is possible to configure i.e If NameNode fails entire cluster goes down, that is why NameNode is called … Continue reading Hadoop 1.x Limitations

Anatomy of File Read

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HDFS has a master and slave kind of architecture. Namenode acts as master and Datanodes as worker. All the metadata information is with namenode and the original data is stored on the datanodes. Keeping all these in mind the below figure will give idea about how data flow happens between the Client interacting with HDFS, … Continue reading Anatomy of File Read

Anatomy of File Write

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Step 1: The client creates the file by calling create() method on DistributedFileSystem. Step 2: DistributedFileSystem makes an RPC call to the namenode to create a new file in the filesystem’s namespace, with no blocks associated with it. The namenode performs various checks to make sure the file doesn’t already exist and that the client … Continue reading Anatomy of File Write

5 Reasons to Learn Hadoop

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Big Data and Hadoop skill could mean the difference between having your dream career and getting left behind. Dice has quoted, “Technology professionals should be volunteering for Big Data projects, which makes them more valuable to their current employer and more marketable to other employers.” 1. Career with Hadoop: According to a Forbes report of … Continue reading 5 Reasons to Learn Hadoop