Hadoop - HDFS Overview Tutorial for Beginners | W3schools

What is HDFS?

HDFS system has been developed to use the distributed file system. It can store huge amount of data-sets and is fault tolerant. It is very easy to access the data as it stored is a systematic order across multiple machines. This kind of data storage helps in preventing data loss due to system failure and improves support for parallel processing as well. The data streaming becomes very simple and easy with this process.

Based on the Google File System, HDFS is written in JAVA language. Though initially built as a storage structure for Apache Nutch search-engine project, HDFS is now widely being used in other domains as well. It was initially termed as Nutch Distributed File System (NDFS).

Also Read: Hadoop Introduction

The main objective of the HDFS is to make an integrated environment for all the computers with a simple storage unit. Hadoop has invented a proprietary algorithm which allows it to run data analysis on multiple computers at the same time, provided they are connected to the Hadoop network.

HDFS Features

Some of the features that make HDFS efficient and easy to use are:

1. Distributed data storage and parallel processing support.

2. Fault tolerant and latency optimized throughput.

3. Simple command interface to interact with HDFS system.

4. Secure systems with built-in authentication systems.

5. the cluster status can be checked at regular time intervals.

6. HDFS node systems monitor the reports send by DataNodes to keep track of failures in the system.

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Advantages of using HDFS

It does not rely on the hardware of the computer to execute its work.

Different servers can be added or removed from its network without having any effect on its working.

It is compatible with almost all the platform as it is Java based.

HDFS Features

Some of the features that make HDFS efficient and easy to use are:

1. Distributed data storage and parallel processing support.

2. Fault tolerant and latency optimized throughput.

3. Simple command interface to interact with HDFS system.

4. Secure systems with built-in authentication systems.

5. the cluster status can be checked at regular time intervals.

6. HDFS node systems monitor the reports send by DataNodes to keep track of failures in the system.

HDFS Compatibility with Big-Data

The HDFS file storage systems are extensively used in analytics field as it deals with big-data. This system is very compatible with a large amount of data because:

1. The MapReduce system is used to access the data in HDFS. This makes downloading and uploading of data extremely fast.

2. The data coherency model of HDFS system is easy to implement, scalable and robust.

3. Installing the HDFS interface is simple and is compatible with any hardware or operating system. Accessing HDFS through a web browser is also straightforward approach.

4. The data is secure and safe as it is stored at multiple locations instead of being concentrated at a particular node.

5. The system is efficient and economical as data can be being processed with parallel computing.