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Hadoop – A Brief Introduction

Hadoop – A Brief Introduction

10/12/2013 6:36 am0 comments

Earlier, it used to be a tough job to store enormous data sets on distributed server clusters. With technological advancements that poured in over the last two decades, however, it has become feasible to both store and analyze big chunks of data without having to shell out hefty budgets.

What is Hadoop and How does it work

One of the amazing techniques that enable easy storage of massive data sets and helps run distributed analysis applications in each cluster unit is known as Hadoop. It IS a big deal in big data and many experts recognize it as a major force.

Let’s get down to the basics.

What is Hadoop?

Basically, Hadoop is an open source software platform. It was introduced by the Apache Software Foundation. It is a simple yet effective technological solution that turned out to be highly useful in managing huge data, a mixture of structured and complex data in particular, quite efficiently and cheaply.

Hadoop has been specially designed to be strong enough to help big data applications run smoothly despite the failure of individual servers. This software platform is highly efficient and does not require applications to transport big data volumes across the network.

How does it Work?

Hadoop software library can be described as a framework which uses simple programming models to facilitate the distributed processing of huge data sets through clusters of computers. The library is not dependent on hardware for high-availability because it can find out and handle failure in the application layer itself. In a way, it delivers readily available services on top of a server of computers that are prone to failure.

Since Hadoop is fully modular, it allows you to swap out nearly all its machineries for a totally different software tool. The architecture is stout, flexible and efficient.

What are Hadoop Distributed File Systems? 

A distributed file-system for the storage of data and a data processing framework are two main parts of Hadoop. These two components play the most important role.

Technically, the distributed file-system is a compilation of storage clusters holding the actual data. Although Hadoop can use different file systems, it prefers to use Hadoop Distributed File Systems (which are cleverly named) for security reasons. Once placed in HDFS, your data stays right there until some operations are required to be performed on it. You can run an analysis on your data or export it to another tool right there within Hadoop.

Hadoop – Data Processing Framework

MapReduce is the default name of the java-based system that works as the data processing framework. We hear more about MapReduce as compared to HDFS  because it is the very tool that actually processes data and is a wonderful platform to work with.

Unlike a regular database, Hadoop does NOT involve queries, SQL (structured query language) or otherwise. Instead, it simply stores data that can be pulled out of it when required. It is a data warehousing system that simply needs a mechanism such as MapReduce for data processing.

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