Understanding Hadoop and Big Data

Hadoop and Big Data

Hadoop is a set of open source programs and procedures that are used in conjunction with big data operations as its backbone. The set of programs and procedures can be modified to fit any company’s needs. The Hadoop backbone helps companies analyze their big data. In the Hadoop program, there are four modules that carry out dedicated tasks and procedures that are essential for a computer system that is designed to analyze big data.

Hadoop Modules

1. Distributed File-System: computer data is stored across a number of linked storage devices and uses an accessible format MapReduce, which provides the basic tools for searching the data. The computer stores large amount of data in a file system that can be accessed and used. The file system a computer uses depends on the operating system installed. However, when a Hadoop is linked to the file system of the host computer, it uses its own file system and sits above the file system of the OS. Any computer terminal that runs the supported OS can access the file system in the Hadoop.

2. MapReduce: the module is named after the two basic operations; it performs within the file system. The module reads data stored in the database, maps the data into a format used to analyze the data, and performs mathematical operations on the data being analyzed. 

3. Hadoop Common: this module provides the tools, written in Java, that are necessary for the end user’s computer to interface and read the data stored under the Hadoop file system.

4. YARN: the module manages the file system resources that store the data and runs the analysis.

Why Hadoop was developed and what were the purposes of Hadoop?

A group of forward thinking software engineers understood that it was becoming necessary for companies to store large datasets for analytic purposes that couldn’t be stored on a single physical storage device. A single physical storage device doesn’t have the capability to access and process large amounts of data. Therefore, it was necessary to design a program that could interface with groups of smaller storage devices working in parallel to form one large storage device.

In 2005 the Apache Software Foundation, that produces open source software and powers most of the Internet released Hadoop. The Hadoop system allows companies to cheaply modify their data system using parts from IT vendors.

Today, Hadoop is widely used by data storage companies to provide an inexpensive way to process and store data across commodity hardware. The program links various off-the-shelf systems together so companies don’t need to invest in bespoke systems custom-made for storing and processing large amounts of data. 

Hadoop in its raw state, uses modules supplied by Apache and can be a very complicated program, even for IT professionals. That is why a different commercial version was developed such as Cloudera. The program simplifies the tasks of running and installing the Hadoop system. Companies can expand and adjust Hadoop with their company’s data analysis operations. Furthermore, Hadoop allows companies to process, analyze, and store big data that they use in their business to prevent data breaches, promote customer services, and predict where the further will head for their products and services.

Image: flickr.com

Top Posts | Software & Method Engineering