Is the Move to Hadoop Best for Your Business?

Many businesses are realizing that their current data mining and analytics methods are not effective in handling a lot of data, and they are turning to big data solutions to meet their needs.


One big data solution that can help businesses store and manage large amounts of data efficiently is Hadoop.

So, what is Hadoop and how can it be beneficial to your business?

Overview of Hadoop

Managed by Apache Software Foundation, Hadoop is an open source software platform that distributes the processing of vast amounts of data across clusters of servers.

This big data platform is efficient and robust. It does not require your applications to send vast amounts of data across your network, and it ensures that your big data applications will not stop running even when the clusters or individual servers fail.

Hadoop has two main components, which are MapReduce and Hadoop Distributed File System (HDFS).

MapReduce is a Java-based tool that performs the processing of data. It functions as a series of tasks, and each task is a separate application that goes into the data and extracts the needed information.

HDFS, on the other hand, is the component that holds the data in a Hadoop cluster. It connects the file systems in the cluster’s nodes and turns them into a single file system.

Benefits of Using Hadoop

There are several reasons why businesses should start moving data into Hadoop faster, and they include:


Big data processing is considered an enterprise IT function, which is traditionally supposed to be expensive. Surprisingly, Hadoop has been proven to be a cost-effective solution. You can download Apache Hadoop distribution for free and start experimenting with it on the same day. The cost of using Hadoop is also kept low by commodity servers, which make it possible for you to create a powerful cluster without having to spend a large amount of money on server hardware.


Hadoop can be scaled from one server to thousands of machines. New notes can be added without having to change data formats, data loading methods or task writing methods. The parallel processing capabilities of a Hadoop cluster can significantly improve the speed of data analysis, enabling you to optimize the use of big data.


Hadoop is able to absorb both structured and non-structured data from an unlimited number of sources. It can join and aggregate data from different sources arbitrarily to enable more in-depth analyses.

Fault Tolerance

Another benefit of Hadoop is its resilience to failure. When data is sent to a cluster node to be analyzed, it is replicated to other nodes. As such, the data will still be available for analysis when a node fails.

Disadvantages of Using Hadoop

While it can be very beneficial to your business, Hadoop also has a number of limitations:

• Unable to handle smaller files efficiently, and therefore, it may not be a good solution for businesses with relatively small amounts of data.

• Comes with certain security risks because its security model is disabled by default.

• Requires expert knowledge to implement and support, and Hadoop skills are in short supply.

The Hadoop is one of the most widely used big data systems, but it may not be a suitable solution for every business.

Weighing the pros and cons of using Hadoop can help you determine whether or not it is the right option for you.

About the Author: John McMalcolm is a freelance writer who writes on a wide range of subjects, from social media marketing to Cloud computing.

License: Creative Commons image source

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