Hadoop File System

Hadoop File System Introduction:

Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part called MapReduce. Hadoop Splits files into large blocks and distributes them across nodes in a cluster. The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data

Overview Of Hadoop File System Job Support:

Hadoop File System was developed by using the distributed file system design. It is run on the commodity hardware. Unlike the other distributed systems, the HDFS is highly fault tolerant & is designed using the low-cost hardware.

The HDFS holds very large amount of data & also provides easier access. To store such huge data, the files are stored across the multiple machines. These files are stored in the redundant fashion to rescue the system from the possible data losses in case of the failure. The HDFS also makes applications available to parallel processing. The Hadoop file system is an highly fault-tolerant, has high throughput, it is suitable for applications with large data sets and streaming access to file system data and it can be built out of the commodity hardware .

The Hadoop provides an command interface to interact with the HDFS. The hadoop file system has the  built-in servers of namenode & datanode that help users to easily check the status of cluster. It also has streaming access to the file system data. The HDFS provides file permissions as well as authentication.

The HDFS is built to support the applications with large data sets, including the individual files that reach into the terabytes. It uses a master and slave architecture, with each cluster consisting of an single NameNode that manages file system operations & supporting DataNodes that manage data storage on the individual compute nodes.

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