How to learn Hadoop: A guide

The big data technology market is expected to reach a staggering $116.07 billion in value by 2027, according to experts. This is a significant increase from the USD 41.33 billion in 2019 and represents a 14 percent compound annual growth rate (CAGR) over the following few years. Big data is being used by more and more businesses, as well as the technology that supports it. Because of this, there are many job openings in the big data sector, particularly for specialists. Currently, becoming a Hadoop expert is a popular career path for job seekers.

Guide to Learning Hadoop

It’s challenging to comprehend and use the framework. But if you have a good teacher, you can learn to do it well. This article answers a variety of questions, including “What are the requirements for Hadoop?,” “Who should learn Hadoop?,” “What are the essential Hadoop tools?” “When do you see yourself using Hadoop in the workplace?” and “What are the use cases?”.

In what ways will knowing Hadoop benefit you?

Hadoop is a game-changing piece of technology that has changed how businesses store and analyze their massive data collections. HADOP, which stands for “High Availability Distributed Object Oriented Platform.”. For managing and analyzing enormous data sets in a parallel processing architecture, Hadoop is a free and open-source software framework. It increases data accessibility for its developers by enabling parallel processing across a set of clustered computers through the distribution of object-oriented tasks. With each node providing its own processing and storage, Hadoop is designed to scale linearly from a single node to a large cluster of computers.

  • It is possible to mine, analyze, process, extract, transform, and load data in the gigabyte, terabyte, or petabyte range from any location, and the network can be watched.
  • Utilizing Hadoop enables businesses to look into complex problems, improving their operational understanding and generating fresh ideas and suggestions for their products.

The likelihood of catastrophic failure is minimized thanks to Hadoop, an all-inclusive open-source library that can detect and fix localized failures. For archiving and analyzing enormous amounts of data, there is a growing trend toward using an ecosystem of Big Data tools and technologies.

  • Hadoop makes it possible for parallel tasks to scale from running on a single server to running concurrently across numerous servers.
  • Its distributed file system makes it simpler to share files among numerous nodes and upload new files more quickly.
  • The computation still proceeds unaffected when a node fails.

Hadoop: History

Hadoop was given the name of a yellow elephant plush toy by Doug Cutting, who was then employed by Yahoo. When Doug was creating Hadoop, his then-toddler son called a stuffed elephant “Hadoop.”.

By distributing data and calculations across several computers, Cutting and Mike Cafarella aimed to increase the speed of search results. Hadoop began as a component of the Nutch search engine project and eventually broke off to become its own distributed computing and processing system.

Hadoop became an open-source project in 2008 thanks to Yahoo. Its maintenance is the responsibility of the Apache Software Foundation, a global volunteer organization devoted to enhancing software.

Does it Take Long to Master Hadoop?

Those who have some programming experience will find Hadoop learning to be much easier to handle. You should select a Hadoop Starter pack that offers programming, analytics, and cloud storage training. The two options available to you are self-study and enrollment in an online course provided by reputable organizations; these courses provide comprehensive instruction in all the fundamentals.

Getting Started with Hadoop: What Should I Do First?

Hadoop has emerged as a top data analytics platform thanks to its adaptability, scalability, and versatility. Numerous employment opportunities have been created by organizations that heavily rely on data processing and the big data revolution, leading many aspirants to start their Hadoop training.

Parts of Hadoop to Study

Although no prior knowledge of Hadoop is necessary to start learning, those with a background in computer science, mathematics, or electronic engineering may find it simpler to understand the framework’s underlying principles. Here are the parts of Hadoop and the jobs they do:

Needed Skills for Mastering Hadoop

While there are no hard and fast requirements to become an Apache Hadoop developer, the following knowledge will make your journey into Hadoop much smoother.

  1. Core Java programming: This is not a strict requirement because Hadoop commands can be written in a wide range of languages, such as C, Ruby, Java, and Pearl. The logic behind well-known Hadoop programs like MapReduce, Pig, Hive, and many others can be better understood by familiarizing yourself with Java’s fundamentals.
  2. Linux: Since Linux is the operating system on which Hadoop clusters are created and managed, having some familiarity with it will be helpful when navigating the Hadoop File System (HDFS). Having staff members with SQL experience—the language used to query databases—can be beneficial for businesses.
  3. High-end data extraction tools like Hive, HBase, Cassandra, and Pig all have command syntaxes that are identical to SQL. This makes it simpler to understand the ideas behind understanding Hadoop skills.
  4. Big data Fundamentals: Just as you can’t find a pearl in the ocean without first knowing how to swim, you can’t use big data effectively unless you first understand its foundational concepts.
  5. When learning Hadoop, it’s crucial to keep in mind that once you’ve mastered the framework, managing large data clusters will be your main responsibility. To succeed academically, you must therefore start with a solid understanding of the fundamentals.

Hadoop and Big Data

Big data technologies have emerged as a result of humans’ inability to keep up with the exponential growth of data available on servers over the past 20 years. Hadoop, an open-source, free distributed processing framework, is at the heart of the growing big data ecosystem.

It is designed to function with cutting-edge analytics tools like predictive analytics, data mining, and machine learning software. Both structured and unstructured data types can be processed using Hadoop. To meet a variety of processing and computational needs, big data technologies like Hadoop collaborate. Hadoop has you covered for handling massive amounts of data.

In industries dealing with a wide range of data, such as mining, manufacturing, information technology, banking, and finance, its superiority has been proven. The essential parts of Hadoop for big data are covered in the aforementioned sections. Let’s look at how Hadoop contributes to the viability of big data.

Use of Hadoop for Analyzing Massive Amounts of Data

Here are some examples of how we can use Hadoop for analytics, taking into account both business requirements and existing data sources.

  • Implementing the Hadoop Framework in Enterprise Data Centers

Hadoop is available for use in private data centers owned by businesses. It is a time and money-saving tactic to use analytical procedures in business. Additionally, the data of the company are kept secure and confidential.

  • Distributor of Hadoop that operates locally

An additional approach to implementing analytics is to use on-premise Hadoop service providers. By offering the necessary tools, applications, and services, they support analytics. For businesses, it increases dependability, safety, and confidentiality.

  • Using resources hosted in the cloud

On affordable hardware, cloud Hadoop services run data and analytics. Although big data processing is affordable, there are some disadvantages, such as security flaws and service interruptions.

Crucial Hadoop Programs to Master

Hadoop is a suite of programs used for a wide range of tasks.


It’s a web application that makes managing Hadoop simpler. Composed of tools for Apache Hadoop cluster provisioning, management, and monitoring A Hadoop cluster’s health can be checked in real time using Ambari’s user-friendly dashboard. One of its characteristics is a metric collection system known as Ganglia.


It is a data warehousing architecture that makes it simple to query and manage sizable datasets stored in distributed storage using the SQL language. Large-scale analytics are made possible by the development of distributed computing and failsafe data warehousing solutions.


on top of HDFS, a scalable distributed table-oriented database. It works well for storing big tables of structured data. HBase can successfully handle specialized data inputs for seamless big data operations. It is a very adaptable and scalable data warehouse with almost instantaneous access that can handle millions of rows and columns in tables.


With the aid of Apache Spark, cluster computing for analytics on massive amounts of data has never been simpler. Workloads with a lot of big data are no issue. It utilizes in-memory cluster computing to store data and repeatedly launch queries on that data. It works well for machine learning because of its quick and effective execution, general batch processing, graph databases, and streaming analytic capabilities.


It is a distributed SQL query engine that is free and open-source and well suited for processing large data sets quickly with little performance hit. This system has the ability to manage and process data from various sources, including Hadoop and Amazon S3.


It is an advanced language and runtime environment for processing and analyzing large data sets using a number of connected data flows. Extensibility, massively parallel processing, and explicit encoding are all characteristics that boost performance.


It is a program that facilitates data entry and exit from Hadoop. It is possible to export data from RDBMSs like Oracle and MySQL back to the RDBMS after moving it from HDFS to Hadoop MapReduce. Parallelism and import mechanisms are regulated by it.


It is a centralized application for configuring, naming, grouping, and synchronizing Hadoop clusters, to put it simply. A service called ZooKeeper enhances the performance, dependability, fault tolerance, and synchronization fundamentals of distributed applications.


On top of Apache Hadoop, the Mahout framework consists of a set of scalable machine learning algorithms. The name is derived from the “Mahawat,” the elephant’s trainer. It orchestrates, to use a metaphor, the Hadoop ecosystem’s operations. Collaborative filtering, classification, and clustering are examples of machine learning algorithms that are used.

GIS tools

These software tools make managing geographical data easier. Geospatial queries using coordinates are managed by Hadoop’s GIS using java-based tools.

Java essentials for Hadoop

There is no hard and fast rule that says you need to know Java before diving into Hadoop. However, getting your feet wet in core Java will help you immensely in comprehending the Hadoop interface.

  • ideas central to object-oriented programming, such as objects and classes
  • Input/output operations with files
  • Managing Unexpected Events and Exceptions
  • Arrays
  • Collections
  • Serialization
  • Expressions of Control and Flow
  • Multithreading
  • Connectivity and Inheritance

Hadoop: Career Scope

Major corporations use Hadoop to carry out petabyte-scale data analytics. Due to the size of the market, developers with expertise in Hadoop and big data can find lucrative employment. Getting an undergraduate degree and developing the fundamental skills and knowledge is usually the first step in professional development. By taking classes and earning certifications, one can gain more knowledge of Hadoop. Being a part of the projects is a fantastic way to use the system in real life.

  • High need for qualified workers:

India’s big data analytics market is expected to be worth USD 16 billion by 2025, according to NASSCOM. As a result, there will be a greater need for skilled Hadoop developers and big data developers. A variety of professions can benefit from becoming proficient with Hadoop tools like Pig, Hive, HBase, and Cassandra.

  • Higher market opportunities:

In a variety of sectors, including but not limited to finance, healthcare, energy, retail, and more, Hadoop is a well-known big data processing platform. Those with relevant certifications and proven proficiency with the Hadoop platform are eligible for the following positions.

  • Hadoop architect
  • Hadoop developer
  •  Big Data analyst
  • Hadoop administrators
  • Hadoop tester
  • Higher pay scales:

With the substantial funding that Hadoop and Big data offer, fund your data analytics projects. The two biggest names in the big data industry, Facebook and Microsoft, are rumored to pay above-average salaries for Hadoop positions. The average salary in India ranges from INR 7 lacs to INR 25 lacs, depending on the individual’s level of experience and expertise.

Cases That Hadoop Shined a Light On

Because Hadoop is so comprehensive, its use has aided numerous industries in opening up new business opportunities. Hadoop has been used in e-commerce, social media, banking, healthcare, and other online platforms to store data, optimize it for search engines, advertise to niche markets, and produce recommendations.

  1. Data Archives: Transactional, social media, and streaming data can all be stored, processed, and combined using Hadoop, and all it needs is inexpensive commodity hardware to do so. Companies can easily archive data with low priority but potential value or analysis in the future using this method.
  2. Data Lake: Analysts can access raw, unprocessed data from data lakes. Working with the original data format allows analysts to pose difficult queries with greater freedom.
  3. Sandbox for data discovery and analysis: By enabling businesses to see previously unnoticed patterns and connections, as well as to gain an unfair advantage over rivals, big data analytics provided by Hadoop can help businesses save time and money. The sandbox approach offers chances for experimentation with little risk.
  4. Energy discovery: Chevron uses Hadoop to sort and analyze seismic data gathered by ships at sea, which may be a sign of the presence of oil reserves.
  5. Internet of Things: Items created using this platform are dynamic and continuously generate a data stream. These enormous quantities of data are stored using Hadoop. Researchers can discover and classify patterns for use in predictive modeling and prescriptive analysis using Hadoop’s enormous storage and processing capabilities as a playground.


Hadoop revolutionizes the big data landscape by opening up powerful computing resources to all users. Using Hadoop, businesses can analyze and query huge datasets in a distributed, scalable, and economical manner. The anticipated rate of industry growth justifies a rise in interest in understanding and utilizing Hadoop-based solutions.