What is Hadoop – The Components, Use Cases, and Importance

by | Aug 27, 2019 | Big Data

9 Min Read. |

Hadoop is an open-source application framework which is a part of the Apache suite of applications. 

It is primarily used for data analysis. It can compute and analyse large amounts of data. 

A primary feature of Hadoop is that it is designed keeping in mind that hardware failures can occur at any time and should be automatically handled by the system. 

Because of its high data processing capabilities and fault-tolerance, it is a popular data analysis software used across various industries.

According to Craig Mundie, Senior Advisor to the CEO at Microsoft, “Data are becoming the new raw material of business.”

Let’s look at what is Hadoop and how it is used by businesses.

What is Hadoop?

Hadoop is a solution to Big Data problems like storing, accessing and processing data. It provides a distributed way to store your data. A data node in it has blocks where you can store the data, and the size of these blocks can be specified by the user.

With it, the data blocks are replicated into Data Nodes, making it extremely scalable. And, new or extra clusters can be added to data nodes as per your data needs.

When it comes to what it does for storing the variety of data, we can safely say that all kinds of data, including structured and unstructured data, can be stored with it.

It also makes data processing easier. With MapReduce, the data is sent to slave nodes to be processed parallel to other slave nodes. All the processed results from the slave nodes are sent to the master node where the data is merged and the result is sent to the client.

Resource Manager is an excellent master node which receives processing requests and sends the relevant parts of the request to node managers, which are responsible for the execution of every data node.

How Did Hadoop Evolve? 

Handling data hierarchy, that is structured or unstructured data, slowed the process of data processing. With the evolution of Big Data analytics, it is possible to increase revenue opportunities and improve customer service. 

But that is the current use of Hadoop. Let us first understand what led to the discovery of Hadoop before we look into how it’s used today. 

Project Nutch was first developed in 2003 as a way to handle all the searches and indexing of web pages. Then, in 2004, Google released MapReduce, and a combination of Google File System and MapReduce was used by Nutch to process data. 

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In 2006, Yahoo developed Hadoop by using the features of MapReduce and Google File System. This framework was used by Yahoo on a 1000 node cluster and was released by Yahoo to Apache in 2008 as an open-source framework. This is how the current Apache Hadoop evolved through trial and error. 

So, what is Hadoop now? It is used to store data in a Big Data environment so that it can be processed parallel to each other. It is a distributed system that can handle thousands of nodes at a time.

History of Hadoop

History of Hadoop Source – Beyond Corner

What Is Hadoop Used For?

Hadoop is not just used for searching web pages and returning results. It is also the next Big Data platform for many organizations. Some popular ways that it is used for today are as follows.

Low-Cost Data Archive

Hadoop’s commodity cost is lesser, which makes it useful hardware for storing huge amounts of data. It can store transactions, sensors, social media, and scientific streams data. Users can store all the data they don’t need at the moment but can be useful in the future.

Integrate with Data Warehouse

It is being viewed as a medium to complement current data warehouses, and many data sets are being offloaded from data warehouses to Hadoop. This is because organizations want a better way to store and process data schemas that support different use cases.

Internet of Things

IoT needs to know when to communicate and act. Iot at its core is a data streaming application containing torrents of data. This is what it is used for in IoT, storing millions of data streams. With its large storage and streaming capability, it is a sandbox for monitoring patterns.

Data Lake

With Data Lake, data can be stored in its original format. This gives data scientists a raw view of data for discovery and analysis. It helps them look at base data without any constraint. 

But, data lakes cannot replace data warehouses, and securing data lakes is time-consuming. Data federal techniques can be used to create logical data structures.

Discovery and Analysis

It was designed with the goal of processing and analyzing huge amounts of data. This design feature can help organizations create more organized data, function efficiently, and uncover new opportunities. 

This gives organizations a great competitive advantage. The sandbox approach also helps in innovation with low investment.

Hadoop Components

The core components of Hadoop include MapReduce, Hadoop Distributed File System (HDFS), and Hadoop Common. These are a set of shared libraries.


MapReduce utilizes the map and reduces abilities to split processing jobs into tasks. These tasks are then run on the cluster nodes where data is being stored, and the task is combined into a set of results.

MapReduce used to be Hadoop’s cluster manager as well as the processing engine that tied HDFS and limited users to process MapReduce batches.

Hadoop YARN is the current Hadoop cluster manager. ‘It’s a job scheduling technology that now functions in place of MapReduce.With YARN, it was integrated with other engines and batch processing applications.


HDFS is a data storage system used by it. The HDFS architecture comprises of Namenode and DataNode that help to implement a distributed file system. The feature provides high performance and scalable Hadoop clusters.

HDFS and YARN are the basic components of it. So, what is Hadoop HDFS? HDFS is the primary component in Hadoop since it helps manage data easily. With HDFS, users can transfer data rapidly between compute nodes. 

When data enters HDFS, ‘it’s broken down into blocks that are distributed to the various cluster nodes.

Hadoop Common

Hadoop Common is the set of Hadoop utilities and libraries that support the Hadoop modules. It is an essential aspect of the framework. It contains Java Archive files needed to start. 

But, what is Hadoop Common used for?

It contains source documents and codes which include different projects from the community.

Like any other module, it assumes that failures are normal and makes provision for these failures. Most of the tools are open-sourced.

For a complete guide to all the Hadoop Common commands, refer the Apache Commands Guide.


Hadoop Source – SCN Soft

Hadoop Use Cases

Knowledge of Hadoop use cases will help you understand what is Hadoop and how it functions in the real-world. It also helps you understand which Hadoop architecture is right for your business so that you ‘don’t make an error is selecting the tools and reducing the system’s efficiency. Here are five real-world use cases.

Hadoop in Finance

Finance and IT are the top users of Apache Hadoop since it helps banks evaluate customer and marketers for legal systems. Banks create risk models for customer portfolios using a cluster.

It also helps banks maintain a better risk record, which includes saving transactions and mortgage details. It can also be used to analyze the world economy and derive value products for customers.

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Hadoop in Healthcare

Healthcare is another major user of Hadoop framework. It helps in curing diseases, predicting and managing epidemics by tracking large-scale health indexes. The main use of Hadoop in healthcare, though, is keeping track of patient records.

It allows for unstructured healthcare data, which can be used for parallel processing. With MapReduce, users can process terabytes of data.

Hadoop in Telecom

Mobile companies have billions of customers, and keeping track of all of them was difficult with traditional frameworks. Take the telecom company, Nokia, for example. 

The company has loads of data from their produced and manufactured phones. All this data is semi-structured and needs to be stored and evaluated appropriately. With HDFS, Nokia can manage and process its data in petabytes, making it easier to consume. 

Call Data Records management, Telecom data equipment servicing, infrastructure planning, network traffic analysis, and creating new products and services are the primary ways it is used in the telecom industry.

Hadoop in Retail

Any large-scale retail company that has transactional data needs data management software. The data comes from various sources and is used to predict demand, creating marketing campaigns, and increasing profit.

So, what is Hadoop used in retail? MapReduce can analyze previous data to predict sales and increase profit. It studies a historical transaction and adds it to the cluster.

This data can then be used to build applications that can analyze vast amounts of data.

Hadoop Use Cases

Hadoop Use Cases Source – Dezyre

Importance of Hadoop

It has become largely popular since the time it was introduced in 2004. This popularity has also made it a must-have skill in the field of Data Analysis. Here are a few reasons it is so important in Data Science


It is an open-source application, which means that anyone can go to the Apache website and download Hadoop. Also, ‘it’s source code is modifiable, and users can change them as per their requirements.

Hadoop Ecosystem

The Hadoop ecosystem is one of the key aspects of Hadoop. Along with storing and processing, users can also collect data from RDBMS and arrange it on the cluster using HDFS.

The Hadoop cluster comprises of individual machines, and every node in a cluster performs its own job using its independent resources.


It can store a large volume of data at a low cost. This is not possible with traditional data storing systems, and scaling high levels of data would be expensive. So what is Hadoop doing different that makes it cost-effective?

For scaling Hadoop, you only need commodity hardware that is available for a lower cost. This also enables unstructured data storage in a cost-effective manner.


‘Hadoop’s distributed file system maps data from wherever it is on a cluster. The data processing servers are usually located on the same server as the data, which enables faster data processing.

It can process terabytes of data within in minutes, and petabytes of data in hours, making it faster than any other data processing system.

Data Handling

It is fault-tolerant; when a data is sent to a node, the same data is replicated to other nodes in the cluster, so even if one node fails, you have the same data on another node for use.


It servers are inexpensive and run in parallel. Large data sets are stored on these servers, so businesses can run applications on multiple nodes for thousands of terabytes of data. This extreme scalability is not available on a traditional RDBMS system.

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With it, businesses can access various types of data and tap into new data sources. This data can be structured and unstructured, and businesses can get valuable business insights from data sources like social media and email conversations.

Log processing, data warehousing, marketing campaign analysis, and fault tolerance are some other ways to use it.

Importance of Hadoop

Importance of Hadoop Source – SoftQubes

To Summarize

Using Hadoop is easy once users get past the initial learning curve. It can be deployed in a traditional data center or in the cloud. 

There are various companies that offer commercial implementation and support, making the transition easier.

Though Hadoop is often used to refer to base modules and sub-modules, Apache states that only software officially released by Apache Hadoop Project.

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