Hadoop Technology Explained For Beginners

Anyone involved in studying or analyzing big data will eventually come across the term “Hadoop.” What does it mean? Hadoop describes a set of procedures and programs engineered on open-source code. As an open source technology, Hadoop is free for anyone to use.

The technology comprises the core of big-data operations, allowing engineers to build a framework on top of the code. Without getting too complicated, we will try to explain the basics of Hadoop technology in this article.

The Introduction of Hadoop Technology

Hadoop found its name from the toy elephant of the son of one of the software engineers working on the project.

The development of Hadoop tech started in 2005 by the Apache Software Foundation. This software company is a non-profit organization that works on open source software solutions. Apache engineers quickly realized the potential of Hadoop as a useful means of analyzing big data sets by individuals not familiar with the specifics of combing through large volumes of data.

Hadoop makes it easy to store and use data on practically any operating system, without the need to use large storage systems. Hadoop allows anyone to store big data on an external hard drive and analyze it. The software enables multiple smaller storage devices to work in parallel, achieving an efficient means of storing and sorting large data sets.

Hadoop – The 4-modules

Hadoop comprises 4-modules, with each module assigned a specific task in analyzing big data. Here is a brief breakdown of each of the modules.

1. The Distributed File-System

The two critical modules of Hadoop are the Distributed File System (DFS,) and MapReduce. DFS allows engineers to store data is an easily accessible manner, over numerous storage devices linked to a network.

The DFS allows you to index all of your data for simple search and reference using MapReduce. A Hadoop system has a separate file system that sits on top of the file system in your computer. This architecture means that any computer or device linked to the system can access the files through the host device using any supported operating system.

2. MapReduce

The second key component of Hadoop is MapReduce. This system allows the user to search through the files and find what they’re looking for in a fraction of a second. MapReduce gets its name from the user functions that the module executes.

First, the module reads the data and presents it on a reference “map,” and then uses an algorithm to “reduce” the data into a suitable format that is easily referenced. For example; if your searching for “females under 30-years old,” the module identifies the data (map) and then reduces it into the requested format (reduce.)

3. YARN

The third module in Hadoop, named “YARN,” manages the resources of the storage system that’s running analysis on the data.

4. Hadoop Common

The final module of Hadoop is Hadoop common. This module provides Java tools for the operating system (Linux windows, iOS,) It allows the system to read the data stored in each Hadoop file system.

Wrapping Up – Earning a Qualification in Hadoop

Qualified Hadoop engineers are challenging to find. If you’re looking for a career with endless opportunity, study a Hadoop training course that qualifies you to work with the technology.

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