Computers and Technology

Introduction To Hadoop: Features & Uses

Hadoop is an open-source platform that allows the storage and processing of large datasets efficiently. In addition, it allows multiple computers to analyze massive datasets rather than using only one computer. There are many Big Data Hadoop Training Institute in Delhi that provide training related to this course. Hadoop consists of four significant modules that are as follows.

  • Hadoop Distributed File System (HDFS)- This file system executes on standard or low-end hardware and ensures better data throughput
  • Yet Another Resource Negotiator (YARN)- This component is responsible for managing and monitoring cluster nodes. Moreover, it also schedules tasks and jobs.
  • MapReduce- It helps in conducting the parallel computation of data. In addition, it intakes input data and converts it into key-value paired datasets. Reduce tasks consume its output to provide the desired result.
  • Hadoop Common- It consists of common Java libraries that can be used in all the modules. It is also known as the Hadoop core and is an essential module of the Apache Hadoop Framework.


Hadoop is an open-source platform that is available online. In addition, various industries can also make modifications to it according to their requirements. It is a highly scalable model as it can be scaled according to the amount of data. In addition, it divides data into inexpensive machines that can be increased or decreased according to the requirements. Many organizational institutions provide Big Data Hadoop Online Course and one can enrol in them to make a career in it. Given below are the features due to which Hadoop is a worldwide popular platform.

  • Provides Fault Tolerance- It uses inexpensive commodity hardware that can crash at any moment. To cope with it, Hadoop ensures that the Data is copied in various DataNodes. This allows easy access to the data from single machinery and in case this machinery fails, one can access it from other nodes in a Hadoop cluster. In addition, this tool makes three copies of data by default and stores them in different nodes.
  • Provides High Availability- As Hadoop ensures fault tolerance, it automatically provides high data availability. In case any DataNode goes down, Hadoop is capable of retrieving it. In addition, it contains two name nodes that are Active name node and passive name node. Passive name node is useful in providing backup for active name node in case it fails.
  • Is Cost-Effective- Unlike relation databases, Hadoop does not use expensive commodity hardware, thus making it a cost-efficient solution. In addition, it is an open-source platform that means free to use. Thus, it ensures cost-effectiveness and makes it a better choice than the traditional databases.
  • Is A Flexible Model- It is a highly flexible model that is capable of dealing with multiple structured, unstructured, or semi-structured datasets such as MySQL Data, XML, JSON. Etc. This flexibility ensures that it can process any data type regardless of its structure. Above all, it becomes more useful for companies as they can use this tool to provide insights of data from sources like social media, email.
  • User Friendly- Hadoop ecosystem comes with a number of useful tools such as Spark, HBase, Mahout, etc. In addition, it makes work easy for developers as it itself manages processing and the developers do not need to deal with the processing work.
  • Provides Faster Data Processing- This tool ensures faster data processing as it breaks the large size file into small size file blocks. After that, it distributes these blocks
    among the Nodes in the Hadoop cluster. In addition, this technique results in providing High-level performance compared to traditional database systems.


Various big data applications use this tool to gather data from various data sources in different patterns. Moreover, large-scale enterprises projects require this technology due to its specialized data management and programming skills. Various large and small organizations use this tool to manage the data. However, this tool works best when the datasets are in petabytes or terabytes. In addition, it can integrate with programming languages like Hadoop can be used with languages like Java, Python, Shell Scripting. Given below are some of the uses of Hadoop.

  • Big Data Storage
  • Business Intelligence
  • Data Mining
  • Analytics
  • Creating forecasting tools
  • Healthcare technologies
  • Anti-fraud analysis
  • Financial and stock markets analysis.

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