Hadoop Tutorial

What is Hadoop and How Does It Work?

Hadoop has become a highly familiar term because of the advent of Big Data in the digital world and establishing its position successfully. The technological development through Big Data has been able to change the approach of data analysis vehemently. However, understanding Hadoop can be critical and if you’re new to the field, you should opt for Hadoop Tutorial for Beginners.

But what is Hadoop and what is the importance of Hadoop in Big Data? Let’s find out from the blog!

What is Hadoop?

Hadoop is a framework that makes use of distributed storage and parallel processing in order to store and manage big data. Data Analysts are the professionals who make use of the software to handle big data. There are three main features of Hadoop which includes:

  • Hadoop HDFS- Hadoop Distributed File System is the unit of storage
  • Hadoop MapReduce- Hadoop MapReduce is the processing unit
  • Hadoop YARN- Yet Another Resource Navigator (YARN) is the resource of management unit.

Advantages of Hadoop in Big Data:

Hadoop is a widely used open-source framework for processing and storing large volumes of data in a distributed computing environment. The advantages will help you to expand your interest in opting for Big Data Hadoop Tutorial for beginners. It offers several advantages for handling big data effectively. Here are some of the key advantages of Hadoop in the context of big data:

Advantages of Hadoop

  • Scalability: Hadoop provides a scalable solution for big data processing. It allows organizations to store and process massive amounts of data across a cluster of commodity hardware. Hadoop’s distributed file system (HDFS) breaks down data into smaller blocks and distributes them across multiple nodes. It enables parallel processing and efficient utilization of resources as the data volume grows.
  • Distributed Computing: Hadoop follows a distributed computing model, which means it can distribute the workload across multiple nodes in a cluster. This parallel processing capability enables faster data processing and analysis, as the tasks can be executed concurrently. Hadoop’s MapReduce framework efficiently manages the distribution of data and computation across the cluster, making it suitable for processing large datasets.
  • Fault Tolerance: Hadoop is designed to be fault-tolerant. It can handle hardware failures gracefully without losing data or disrupting ongoing processes. When a node fails, Hadoop automatically redistributes the data and tasks to other healthy nodes in the cluster. This fault tolerance feature ensures high availability and reliability of data, which is crucial when dealing with large-scale data processing.
  • Cost-Effectiveness: Hadoop is based on commodity hardware, which is less expensive compared to specialized hardware or high-end servers. It leverages the power of distributed computing using cost-effective hardware components, making it an affordable option for organizations dealing with big data. Additionally, Hadoop’s ability to scale horizontally by adding more nodes to the cluster allows organizations to expand their data processing capabilities without significant upfront investments.
  • Flexibility: Hadoop provides flexibility in terms of data types and sources it can handle. It can process structured, semi-structured, and unstructured data, allowing organizations to work with diverse data formats. Hadoop’s schema-on-read approach enables users to store raw data without predefined schemas and structure it at the time of analysis. This flexibility is beneficial in scenarios where the data is constantly evolving or when dealing with complex, heterogeneous data sources.
  • Data Processing Ecosystem: Hadoop has a rich ecosystem of tools and frameworks that complement its core functionalities. For example, Apache Hive provides a SQL-like interface to query and analyse data stored in Hadoop, while Apache Spark offers fast in-memory data processing and machine learning capabilities. These additional tools enhance the capabilities of Hadoop and provide a comprehensive platform for big data processing, analytics, and machine learning.

How does Hadoop work and how to use it?

Hadoops basically runs on commodity servers and it can scale up for supporting thousands of hardware nodes. The file system is designed for providing rapid data access across the nodes in a cluster along with fault-tolerant capabilities because applications can continue to run in case anu individual nodes fail. These features help Hadoop become a foundational platform for Data Management for the use of Big Data Analytics after it emerged in the mid-2000s.

Here is a brief about how Hadoop works:

HDFS: Hadoop maintains data in a manner that is distributed using the Hadoop Distributed File System (HDFS). The file is broken into smaller sections, which are typically 128MB or 256MB in size. These blocks are subsequently dispersed among the Hadoop cluster’s nodes. For tolerance for faults, each record is replicated across multiple nodes, often with a replication factor of threefold.

Data Processing using MapReduce: Hadoop utilises the programming model known as MapReduce to process data. MapReduce separates processing into two phases, which are Map and Reduce.

  • Map: The input data is separated into sections and handled in parallel across the cluster nodes throughout the Map phase. Each node performs a map operation on its own data chunk, transforming it into pairs of keys and values.
  • Shuffle and Sort: After the map phase, the intermediary key-value pairs generated by each of the nodes have been organised and grouped across the cluster of nodes by key. This is known as shuffle and sort.
  • Reduce: The sorted preliminary key-value pairs will be processed in the Reduce step to obtain the final result. Any node reduces a portion of the key-value pairs, combining or summarising the data as appropriate. The reduction phase output usually gets saved to a file in HDFS.

Job Submission and Cluster Management: To take advantage of Hadoop, you generally use the Hadoop API to generate code in Java, Python, or other compatible languages. The code has been compiled and saved as a JAR file. You next publish your Hadoop task to the Hadoop cluster, which involves the JAR file and input/output directories. The Hadoop cluster management spreads jobs among accessible nodes, organises execution, and manages tolerance for errors.

Challenges of Hadoop and How we Solve Them:

Although Hadoop is one of the excellent technologies that has enabled big data environment feasible, it has certain limitations which has complicated its use. Following are some of the challenges that you might face as a user with Hadoop:

  • Performance Issues: Hadoop’s dependability on disc storage for data processing might result in lower performance as compared to systems that use memory-intensive processing, such as Spark. MapReduce, Hadoop’s common processing engine, frequently requires lengthy disc reads and writes, which can add delay and lower overall speed of processing. Spark’s ability to capitalise on stored in memory data processing, on the opposite hand, gives considerable enhancements in speed for many industrial applications. Spark decreases disc I/O operations by retaining data in memories, resulting in shorter processing times.
  • High prices: Because Hadoop’s structure tightly combines both storage and computing resources, it can result in higher prices. When processed or data storage needs increase, organisations frequently need to add more cluster nodes, which are which implies scaling both processing and storage at the same time. This strategy may result in excessive resource allocation, as expanding nodes to meet rising storage needs additionally results in excess computing capacity. Separating compute and storage, on the other hand, allows organisations to grow every resource individually, minimising costs based on the particular needs.
  • Excess Capacity: One result of combining both computing and storage resources in a cluster of Hadoop servers is the possibility of excess capacity. When extra nodes are installed primarily to boost the amount of storage, their processing resources might remain unused. This mismatch among storage and computing power might result in extra expenditures and maintenance.
  • Management Difficulties: Organisations frequently confront difficulties while establishing and managing big Hadoop clusters. When other big data technologies are integrated into the Hadoop ecosystem, the complexity grows. Aside from cluster management, responsibilities like data integration and data quality control can be difficult for organisations that use Hadoop systems.
  • On-site orientation: Hadoop was designed initially for on-premises installations. While all of its elements can now be found in stored in the cloud big data platforms, Hadoop remains largely an on-site solution form.


The above blog gives you a clear idea about the Hadoop tutorial for beginners and its uses. It certainly ignites your interest in Hadoop Learning for Beginners. Accordingly, you will be able to expand your knowledge in Hadoop and learn about its application in real-world cases. You will find numerous online courses that offers you Big Data Hadoop training in India. while opting for Data Science courses, you will also find short-term courses that offers you with Online Hadoop Certification Training.


Is Hadoop Java-based?

The platform of Hadoop is java-based and the codes written in the software are both in Java and C Language. A basic knowledge of object-oriented programming concepts, file structure and error handling which are enough to understand the ecosystem. Although knowing advanced java concepts is not important but can be an added benefit in your role as a Hadoop developer.

How Long does it take to learn Hadoop?

If you are involved in self-learning, you can learn Hadoop within 3-4 months. However, in case you opt for Online Hadoop certification training, one can easily master Hadoop in 2-3 months.

Is Hadoop a good career option?

As the job roles in Big Data Analytics is in great demand in the market today, Hadoop job roles are a great attraction for the aspirants. Thus, you will have various offers of job roles with promising salaries.


  • Aishwarya Kurre

    Written by:

    I work as a Data Science Ops at Pickl.ai and am an avid learner. Having experience in the field of data science, I believe that I have enough knowledge of data science. I also wrote a research paper and took a great interest in writing blogs, which improved my skills in data science. My research in data science pushes me to write unique content in this field. I enjoy reading books related to data science.

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