In the world of big data, it’s not the closed-source software that’s making waves. Rather, the open-source community is the one that’s pushing boundaries. And, in the open-source space, it’s Hadoop that’s become everyone’s darling big data app. But, just what is Hadoop?

What Is Hadoop?

Hadoop is the name given to an Apache open-source software framework. This framework consists of a distributed file system similar to Unix. The main difference is that the data storage is spread out over multiple machines instead of being concentrated on a single machine.


This setup carries several important advantages. First, there is no single point of failure. Secondly, the framework is made up of multiple datanodes (places where data is stored), providing either redundancy or diversity in file system allocation.

Hadoop also uses a name node – a “master machine.” You could think of this as “one machine to rule them all.” Its job is to control all of the meta data for the cluster. Finally, there’s a secondary namenode which, non-intuitively, is not a backup node but rather a form of data logging. It keeps a copy of the edit logs and file system image.

Data on these systems are accessed via Java API or the Hadoop command line client. Most of the operations you find in Hadoop are similar to their Unix counterparts.

Hadoop And Big Data

For business owners and large corporations, where the rubber meets the road is in the application of analytics apps like Hadoop. The truth is that there’s so much hype surrounding this software that many businesses mistakenly believe that it’s capable of anything and everything.

But, it’s incredibly complex, with IT support necessary even for basic setup. It’s also why educational organizations like Simplilearn provide extensive Hadoop training. It’s just not something that a DIY’er could pick up and figure out by himself.

And, while the software does have a steep learning curve, it does offer one huge advantage: big data integration. For companies that require massive data merges, scalable nodes without having to change data formats or how data is loaded, or how jobs are written and that require a low cost per terabyte for storage, it’s hard to beat Hadoop.

Usually, modeling is expensive, just because of the complexities of data storage and management, scaling, and integration. Legacy systems are often the culprit when a company expands as they cannot easily be integrated into new analytics software.

Moreover, systems of the past were hardware driven, whereas Hadoop is software driven. That means the application can use commodity servers, dramatically reducing the cost for data storage.

Hadoop is also schema-less, accepting any form or type of data. It can even be unstructured data, which is important as many new metrics are being discovered on social media platforms which inherently use unstructured data in the form of status updates, image uploads, and videos.

This type of data can be joined and aggregated in an arbitrary fashion, making deeper analysis possible and providing new sales and marketing predictions that were never possible before.

For example, if a company wants to analyze and cross-reference all posts, images, videos, and status updates across multiple social media networks for a target market, Hadoop could collect the data and aggregate it according to any number of arbitrary criteria to find users with particular beliefs, buying habits, preferences, and possibly even marital status, income level, and values.

How Hadoop Helps

Hadoop has a high fault tolerance, meaning that if you lose a node, your system doesn’t crash. It simply redirects work to another location, retrieves the relevant data, and processes it accordingly.

This is in stark contrast to previous big data applications, which relied heavily on hardware. Ultimately, the biggest benefit for businesses is the flexibility and scalability of a software-driven analytics tool.

But the tool isn’t for everyone. Hadoop shines when you need to process large swaths of data. If, for example, you’re aggregating a large pool of data from previous customer interactions, purchasing history, social media data, and meta data from other sources, Hadoop can help.

But, if you’re looking to analyze incremental changes in your dataset, don’t bother. Hadoop won’t process these as efficiently as your current database program will.

Hadoop is used in large-scale projects. These projects are almost always made up of clusters of servers and require specially trained employees (engineers) with specialized programming training and education. The overall total costs can skyrocket if you’re not careful, even as the unit cost drops like a stone.

About the author: Chandana is working as a Content Writer in Simplilearn.com and handles variety of creative writing jobs. She has done M.A. in English Literature from Gauhati University. A PRINCE2 Foundation certified, she has a unique and refreshing style of writing which can engross the readers to devour each sentence of her write-ups.

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