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Using Hadoop For Big Data Concerns

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Hadoop For Big Data ConcernsThe volume of data handled by different individuals, firms or corporations varies. Data can be said to be big when it reaches a point where you cannot handle it. Efficiency in data management arises when you are able to receive pockets of data, process and store it accordingly. Big firms normally outsource data management services so they do not have to deal with the cost of processors and cloud storage needs.

At times you can get frustrated and annoyed at the inability of a system you have spent so much money on. The good is news is that data storage is now cheaply available. The downside is that processing large amounts of data is still a challenge to many. You can find yourself easily overwhelmed. Before you are through with yesterday’s data, today’s starts trickling in.

In order to process large amounts of data, you need a big enough server. That is where Hadoop comes into play. It is an effective tool for processing as well as storing big data. Instead of relying on one computer, Hadoop utilizes hundreds or even thousands of them. Below are some of the ways through which Hadoop handles data problems:

Getting to understand big data

Traditionally, you have been relying on tools such as spreadsheets and SQL databases for data analysis and storage. Judging by the amount of data generated today, these cannot sufficiently deal with such herculean volumes. Today, we can measure data in zettabytes. To write this in figures, it is 1 and 21 zeroes. Now, that’s big. To demonstrate its vastness, take for instance an article like this one. Three paragraphs of text amount to around 1 kilobyte. Imagine if you were to write 3 paragraphs for each grain of sand found on this earth. Well, that is how big a zettabyte is.

The Anatomy of Hadoop

Considering the magnanimity of data, Hadoop tackles the problems by using thousands of computers to handle big data concerns. This makes it fast to mine, analyze or process data since the more you process the more it is generated. The name Hadoop comes from that given to a toy elephant belonging to the son of one of the Google programmers behind the project. The group of computers mentioned above is referred to as a cluster. Each computer in a cluster is known as a node.

Benefits of Using Hadoop

  1. Affordability: Being an open source project makes its usage free. It runs on normal computers like the one you are using at the moment. There is no need of buying sophisticated machines.
  2. Speed: This is a fast system. It takes a matter of minutes to tackle a terabyte and several hours for a petabyte. That is how companies such as Facebook, Yahoo, Twitter, Amazon and eBay manage to handle massive data and generate decisions quickly.
  3. Scaling data: If at all you need extra space, you only have to add a hard drives to nodes or nodes to clusters. Hadoop cannot shut itself down.
  4. Flexibility: Irrespective of the type of data you are dealing, this application will handle it effectively. Whether your data involves documents or just figures and numbers, Hadoop will process and store it all.
  5. Programming languages: The application is in Java programming language. Data is accessed through Apache Hive, an SQL based language. There is also Apache Pig for further language analysis. In simple terms, Hadoop integrates easily with other programming languages.

Hadoop is easy to learn for many data experts and the cost is reasonable for cloud storage needs. If you want to keep your data secured, working with the right company can make a huge difference.

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