What is Apache Hadoop in Data Analytics – Data Science Jargon for Beginners

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Updated December 2, 2017

What is Apache Hadoop?

First, it is not a database, new coding language, or cloud server.

Hadoop is an application that utilizes available server space to store and distribute data across multiple servers, in order to create a mean time to failure system (MTTF), and speed up the processing speed of data analysis.

Basically we can all go home now. That is as simple as it can be stated. But, for the sake of explanation and clarity I want to break this down a little more….

1 ) Hadoop utilizes available server space to store and distribute data

In order to create a high performance single server you would have to pour tons and tons of RAM, Memory, and Drive into that server. Not only is this expensive but this techniques allows no backup system for your very important data. In order to get high performance at a low cost and low failure rate Hadoop distributes data across server clusters (multiple servers tied together via physical or cloud-based).

By spreading the load across multiple servers it makes your retrieval rate for data sky rocket!

Hadoops first major benefit is its power to store and retrieve very large sets of data quickly.


2 ) Hadoop distributes data across multiple servers in order to create a mean time to failure (MTTF) system

Let’s say you have a very large data set of email addresses. I am talking 100,000’s of email addresses. It is the day before black Friday and your server goes down… With all your data on one server you are now unable to send out a black Friday email to all of your subscribers, which means you could lose millions of $DOLLARS$ in sales in one day!

If you have all of your email addresses stored on a single server your MTTF is extreme fragile. But when you distribute them across a cluster of servers while using Hadoop to store them for fast acquisition your email list is safe from danger.

3 ) The ability to process data is faster using Hadoop

Because Hadoop uses a cluster to store data there is more computing power to establish faster processing. Also, Hadoop uses a technique known as MapReduce to extract data faster. For a full description of MapReduce check out our post Data Jargon – MapReduce. Using MapReduce allows Hadoop to break apart data sets into smaller components and spread them across multiple servers to improve the speed of the process.

Speed is improved because of data availability. When the data is spread across multiple servers it can be accessed quickly.

Don’t confuse data extraction with the process of analyzing. Hadoop is a fantastic resource to house data for quick extraction. But if you are looking to view data and come up with valuable insights you are going to want to combine Hadoop with Tableau or OpenRefine.


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