In the past couple of years, the most talked about two new terms in the Internet community were—Big Data and Hadoop. This write-up helps readers understand what the meaning of these two terms is, and how they impact the Internet community not only in the present times but also in the coming future.
|Big Data processing and storage engine||Hadoop|
|Creation of Big Data||Occurred 90 percent in last two years|
|Common Big Data NoSQL databases||MongoDB, Cassandra, and HBase|
Watch this Hadoop Tutorial for Beginners video
What Is Big Data?
Big data is a term that describes a large volume of data. It can be structured or unstructured. But it’s not the amount of data that’s important. The real concern is, what organizations do with this data. Big data can be analyzed for insights which lead organizations to take better decisions and also help them in making strategic business moves.
Big data is typically characterized as 3 Vs which are as follows:
- Volume: Data is collected from a variety of sources, which may include business transactions, social media, and even information from sensors of organizations. Storing these data had been a problem in the past. But new technologies (such as Hadoop) have made this an easy task.
- Velocity: Velocity typically indicates the rapid speed at which data is transferred or received. This can be understood just by visualizing the amount of data—in terms of likes, comments, video uploads, tags etc.—that is handled by the social networking sites like Facebook in just one hour.
- Variety: Data can be seen in any type of formats. It can be in structured format, like the numeric data in traditional databases, or in unstructured format, such as, text, email, video, audio, or data from some financial transactions.
Making Use of Big Data
As mentioned earlier, the importance of big data doesn’t lie in how much data you have, but it revolves around what you do with it. One can take data from any source and analyze it to find answers that enable:
1) Reduction in cost
2) Reduced production time
3) Development of new products
4) Smart decision-making
Big Data Challenges
Major challenges which are associated with big data are as follows:
- Capturing data
- Storing data
- Searching data
- Sharing data
- Transferring data
- Analysis of the previously stored data
Normally, organizations take the help of enterprise servers in order to fulfill the above challenges.
How to Deal with Such a Large Amount of Data?
Google solved the problem of dealing with huge amounts of data, which was really a tedious task with the help of a traditional database server by using an algorithm called MapReduce. This algorithm divides the task into small parts; those parts are assigned to many computers which are connected over the network, and these computers collect the results to form the final result dataset.
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