Is Hadoop Easy to Learn? How Do I Start Learning It?

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Big Data adoption has gained traction over the past few years, and at the top of the Big Data technology, the pyramid is Hadoop. This open-source tool from Apache offers a powerful suite of programs to process large datasets. 

As companies handle more and more data from multiple sources, they look for high-performance computing tools that can solve Big Data problems. Internet giants and global enterprises gain enormously from the distributed computing capability of Hadoop and its inherent powerful computing. The popularity of Hadoop has driven a steep demand for Hadoop skills. As a result, knowledge of Java, the Hadoop underpinning, and other core components such as the HDFS, MapReduce, and YARN continue to be in high demand in the Big Data job market.

With Hadoop becoming the ultimate standard for handling huge data, more and more companies are deploying Hadoop to process and crunch Big Data across industries.  Some of the global names hiring Hadoop skill sets are eBay, Facebook, Twitter, Google, Yahoo, LinkedIn, Spotify, IBM, and NAVTEQ, to name a few. This is a good reason why any IT or Data Science professional must learn Hadoop basics and increase the chances of landing a job in such reputed organizations. 

IT executives who want to prepare for a fast-growing job market and upskill in an ecosystem that is critical for Big Data processing will include Hadoop basics in their learning curve. 

What is Hadoop?

Hadoop was introduced by the Apache Foundation in 2008, developed as an open-source software framework to handle the Big Data ecosystem. It processes large datasets across clusters of commodity hardware, handling huge tasks and applications concurrently. This allows scaling-up from single servers to thousands of machines by adding nodes in the cluster. Hadoop can thus store and process large datasets ranging in size from gigabytes to petabytes of data. 

The two key layers of Hadoop are:

  • The Processing or the Computation layer: It works on MapReduce, the Parallel Programming Algorithm for distributed applications, first introduced by Google.
  • The Storage layer: Also known as the Hadoop Distributed File System (HDFS), it is based on the Google File System and is designed to run on low-cost commodity hardware.

Other components of the Hadoop architecture include

  • The Hadoop Common ecosystem of Java libraries and utilities, and
  • The Hadoop YARN framework for job and cluster resource management.

Hadoop employs the YARN architecture to split the functionalities of resource monitoring and job scheduling.

The Hadoop framework is used for batch or offline processing, stream processing, interactive processing, and graph processing which are stored in the HDFS cluster.

Is Hadoop Easy to Learn?

Hadoop is not very difficult to learn if you can begin with the fundamentals like Java, SQL, and database administration and move up towards the more complex part. Of course, you must master the concept of Big Data and learn how different frameworks within Hadoop provide solutions from raw and streaming data. Knowing about the various components of Hadoop and their uses in solving different problems makes it easy for you to apply Hadoop basics to the Big Data tasks at hand. 

To make your Hadoop learning easy, adopt a structured learning path at a recognized institute and follow the instructor-led training.

How to Start Learning Hadoop?

You can learn Hadoop by either of the two methods:

  • Self-learning
  • Instructor-led learning

Learning Hadoop on your own is time-intensive. It also depends upon your level of intellect and skills working with various languages and tools. However, instructor-led learning from experts is always a preferred way to fast-track Hadoop basics. You master all the necessary skills, together with both academic and practical Big Data training. Gaining a Hadoop Certification takes less time too. All you need to do is register for a Certification in Hadoop basics.

Hadoop does not require much coding as you work on Pig and Hive which involves only a basic knowledge of SQL. However, there are some languages and other fundamentals you must master as part of the Hadoop learning path.

So, once you have decided you want to learn the Hadoop basics, navigate through the following processes:

Know Your Java

Java programming language forms the mainstay of Hadoop. The commonly used Sequence File format as well as the UDF, are Java-dependent. Familiarity with the Java language and the core APIs is enough to have you work with Big Data.

Fine-Tune Your SQL Skills

SQL is a must-learn as Hadoop uses SQL commands and queries to process Big Data components. Besides, the Hadoop ecosystem has many software packages like Apache Hive and Pig that extract data from HDFS using SQL-type queries. 

Get Hands-On with LINUX and Ubuntu

Working knowledge of Linux OS helps you to install the Hadoop framework on your computer and for file management, whereas Ubuntu helps you in server distribution. 

Learn Cloud Computing

Master cloud technologies and techniques to help you with functions that recall, manipulate, and interpret data in various cloud formats.

Build Strong Foundations in Data Management

As Hadoop crunches a massive amount of data, you must learn the basics of database management, and what techniques to use for different types of data problems.

Hone Your Analytical Skills

Working on Hadoop requires sharp analytical skills. So, brush up on your statistics and math. 

Learn Task-Driven Programming Skills

Hadoop can handle different programming languages like C, C++, Ruby, Groovy, Perl, and Python depending upon the task at hand. So, for instance, a Hadoop developer must know Java or Scala, while a Hadoop Tester must add NoSQL, HBase, and Cassandra, to his Hadoop arsenal.


With Hadoop emerging as the de-facto tool for working on large datasets, companies in India and across the world are favoring the use of Hadoop as their default Big Data platformIf you want to master the Hadoop basics, start with a project that implements Hadoop. Practice your Hadoop skills on the go and prepare yourself for a future in a Big Data job market.

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