5 Things One Should Know About Hadoop

Date Published: 11-4-2012
By:

Big Data is a term used to categorize an excessive amount of aggregated data. But, how do Data Miners manage all of this data? Hadoop is one of the popular tools that data analysts are using to store and mine immense volumes of data.

Here are 5 Things a Data Analyst should know about Hadoop:

1.      Hadoop utilizes parallel processing to store and process massive amounts of data. As open sourced software, Hadoop eliminates up front licensing costs for managing and processing large volumes of data. In other words, anyone can use Hadoop on any computer – for free. Other cost advantages include the fact that Hadoop can exist in a stand- alone environment, eliminates inter-processing communication between mappers. Finally, one cannot overlook that the Hadoop ecosystem creates a stable, fault tolerant environment.

 

If one is an expert computer programmer, Hadoop will serve to be extremely helpful.

2.      Multiple analytical uses is one of Hadoop’s most valuable features such as: text mining, risk assessment, sentiment analysis (link to text analytics), predictive models, pattern recognition, and index building.

 

3.      Hadoop is also very fast in analyzing and managing terabytes of data in minutes, which is great for social media or retail data. Speaking of which, Hadoop can handle many types unstructured and semi-structured data, including but not limited to: web logs, system logs, audio/video, email, transactional data.

 

4.      The Hadoop ecosystem is made up of several parts that make the Hadoop File System run smoothly and quickly. These parts of the ecosystem have fun names like, Pig, Hive, Oozie, Flume, and if one is a computer expert, one can customize each component to fit their need.

 

 

Therefore…

 

5.      The user needs understand or have some background in computer programming in order to use Hadoop properly. Prerequisites include being knowledgeable in Java and/or other programming languages to run data processing jobs. If an organization does not have in-house expertise, there will be a cost to customize. However, the cost may be worth it – dismissing the potential goldmine in a company’s data may translate into untapped opportunity, opportunities that your data savvy competitors may discover.