What is Hadoop

Are you looking to kick start your professional career as a Hadoop developer? The very first question that comes to your mind is that, what is the Hadoop tool? And why is that so important? No need to worry, we are here to help you to answer all your questions. In this blog, we are going to explain what is Hadoop, architecture, components, features, and HDFS modules. Hadoop is an open-source framework mainly used to store and process big data across multiple clusters using simple programming models. As we know that Hadoop is emerging as one of the top framework tools mainly used in big data architecture. Let’s begin;

What is Hadoop?

Hadoop is an Apache product and this is nothing but a collection of open-source software framework utilities that provides a network of many computer devices to solve complex problems which involve a massive amount of data and perform computations. This offers a storage framework to process the big data modules with the help of MapReduce programs. This Hadoop also offers massive data storage for any kind of data, enormous data processing power, and allows users to handle enormous concurrent tasks or jobs. This Hadoop framework is written using programming languages like Java, but never makes use of OLAP or online analytical processing. This is used for batch or offline data processing. Hadoop is used by major companies like Facebook, Yahoo, Google, LinkedIn, and many more to store a large volume of data.

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Why do we need Hadoop?

The following are the key reasons, which will explain why we need to know about Hadoop:

1. One of the important advantages of Using Hadoop is its cost-effective nature when compared to other traditional database technologies in storing and performing data computations.

2. Hadoop comfortably accesses different kinds of business solution data and also proved its benefits in decision making.

3. This Hadoop also acts as an enabler for social media, log processing, data warehousing, emailing, and error detection.

4. By mapping the data sets wherever it is placed, Hadoop technology minimizes the time taken to unfolding any data sets. It can also work on a large number of petabytes of data on an hourly basis and makes it super-fast.

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Introduction to Hadoop Architecture:

In this section, we are going to explain the basic functionalities, components, and work nature:

The following diagram will explain the architecture overview of Hadoop:

Hadoop Architecture

Hadoop architecture is the same as Master-slave architecture for data transformation and analyzing large volumes of data sets by using Map reduce Paradigm. There are three important Hadoop components available and they play different roles in the architecture are;

1. Hadoop distributed file system or HDFS – this is a type of pattern used in UNIX file systems.

2. Hadoop Map reduce components

3. Resource negotiator or YARN (Yet another resource negotiator).

When you start to learn about Hadoop architecture, every layer in Hadoop architecture requires knowledge to understand various components. These components perform operations like designing the Hadoop cluster, performance tuning, and chain responsible to perform data processing. As I said earlier Hadoop architecture follows master-slave architecture design containing master node or name node, where master node is used for parallel processing of the data by using a job tracker.

Introductions to the Hadoop file system:

Hadoop file systems are specially developed by using distributed file system management design. This is usually running on commodity hardware. HDFS module is highly faulted tolerant and low-cost hardware design. HDFS consists of a large volume of data and offers easier data access. To store a large amount of data, the files are used to store these data files across multiple machines. The HDFS file is usually stored in a redundant system to overcome possible data losses and reduce failure. HDFS also makes any application that is available for parallel processing.

Important features of HDFS:

The following are the important features of HDFS namely:

1. HDFS is suitable for distributed data processing and storage management.

2. Hadoop HDFS also provides a command interface to mainly interact with multiple systems.

3. The built-in servers like name node and data node help users to easily check the cluster status.

4. Offers data streaming to access various file system data.

5. HDFS module offers mechanisms like file permission and authentication service.

Goals of HDFS module:

The following are the important goals of the HDFS module:

1. Helps in fault detection and recovery: HDFS module consists of a large number of commodity hardware resources and components failures. So HDFS should support various mechanisms to quick defect, recovery, and automatic fault detections.

2. Helps to store huge data sets: HDFS consists of hundreds of node clusters, which are mainly used to manage the application database that holds huge data sets.

3. Offers hardware capabilities at data: Here a requested task will be done efficiently only during the time of computational tasks. This happens especially where large volumes of data sets are used; this reduces the network data traffic and increases them throughout the process.

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Important operations of HDFS:

The following are the major operations of the HDFS module:

1. Starting with HDFS:

This is an initial stage, where you need to format the configured file system (HDFS file types), open name node cluster or HDFS server, and executes the below command:

$ hadoop namenode   - format

This command formats the HDFS cluster node, and then it will start the distributed file system. The following command will start the name node in the cluster;

$ start – dfs. Sh

2. List the files in HDFS:

This step will occur, once you load the information into the server system, you are able to find the list of files in a record, or directory, a file status by using “ls”. The following syntax helps to pass the data to a directory or a file name in the argument.

$ $ HADOOP _ HOME /bin/hadoop fs   -ls

3. Inserting the data into HDFS:

Consider that we have data in the file system called file.txt in the local system directory and only saves the HDFS file system. The following are the important commands used to perform these operations:

$ $HADOOP _ HOME /bin/hadoop fs  -mkdir /user/input

4. Retrieving of data from HDFS:

Usually, a file in HDFS is called outfile. Below are the simple commands used to perform the operations in the Hadoop file system.

Firstly, view the data from HDFS by using cat command:

$ $HADOOP _HOME /bin/ hadoop fs –cat /user/output/outfile

Now gets the file from HDFS to the local file system using the “get” command

$ $HADOOP _HOME /bin/hadoop fs –get/ user/output /   / home /hadoop_tp/

5. Shutting down the HDFS file system:

With the help of the below command you can shut down the file system:

$ Stop-dfs.sh

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HADOOP module – YARN:

YARN is nothing but yet another resource manager that helps to take the programming to the next level and makes this programming application interact with another application Hbase, and SPARK, etc. One more important point to be considered here is that different YARN applications can exist on the same cluster for example Map Reduce, Hbase, and Spark run at the same time to offer manageability and clustering utilization benefits.

Important components of YARN:

1. Client: This component is used to submit the Map-reduce jobs.

2. Resource manager: this component is used to manage the resources across the cluster.

3. Node manager: used for launching and monitoring the computer containers on various machines in the cluster.

4. Map Reduce application master component: this will check the tasks which are running the Map-reduce job. The application master and the Map-reduceMap reduce tasks both will run in containers scheduled by the resource manager and managed by node managers.

In the previous Hadoop version, Job tracker and task tracker were used, which were used to handle resources and check the progress management. The latest version of Hadoop 2.0 consists of resource manager and Node manager to overcome the shortfall of the Job tracker and task tracker components.

Benefits of YARN:

The following are the important benefits of YARN:

1. Offers high-level scalability: Map-reduce 1.0 offers scalability that consists of 4000 nodes and 40000 tasks, but here YARN is designed for 10,000 nodes and 1 lakh major tasks.

2. Better Utilization: here the node manager helps to manage a pool of hardware resources, other than fixing the number of designated tools so this increases the utilization.

3. Multitenancy: Different versions of Map-Reduce will run on YARN, this makes the upgrading process of Map Reduce.

Advantages of Hadoop:

The following are the few benefits of using Hadoop:

1. Hadoop is a moving computation, so it’s always good to use moving computation instead of moving data.

2. Hadoop framework runs on commodity hardware.

3. Hadoop architecture is a master or slave type of framework.

4. This tool helps to handle all the types of node failure in the cluster.

5. The one and only program you need to concentrate on while working with Hadoop is Big data.

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When you hear the word Big data, your head turns and started to think about it. I hope you got my point why I use these words because Big data technology is a popular product and developed with the help of Hadoop technology. Why do you need to learn Hadoop big data? The answer would be, to get appropriate and error-free data. As per the latest research, we generate almost 20 GigaBytes of data per day, so it’s always good to process the data to get accurate data sets. From this Hadoop blog, you will be able to learn what Hadoop is, YARN module, architecture, and HDFS. You can expect huge job openings for Big Data analysts or Hadoop professionals across the globe. 

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As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.