Let us try to understand what is big data. Big data basically refers to huge volume of data that cannot be stored and processed using the traditional approach within the given time frame. The next big question that comes to our mind is how huge this data needs to be in order to be classified as big dat. There is a lot of misconception while referring the term Big Data. We usually use the term Big Data to refer to the data that is either in gigabytes, terabytes, petabytes, exabytes or anything that is larger in size. This does not define the term Big Data completely. Even a small amount of data can be referred to as big data depending on the context it is being used. Let me take an example and try to explain it to you. For instance if we try to attach a document that is of 100 megabytes in size to an email we would not be able to do so as the email system would not support an attachment of this size therefore this 100 megabytes of attachment with respect to email can be referred to as Big Data. Let me take another example and try to explain the term Big Data. Let us say we have around 10 terabytes of image files upon which certain processing needs to be done for instance we may want to resize and enhance these images within a given time frame. Suppose if we make use of the traditional system to perform this task we would not be able to accomplish this task within the given time frame as the computing resources of the traditional system would not be efficient to accomplish this task on time. Therefore this 10 terabytes of image files can be referred to as big data now. Let us try to understand big data using some real-world examples. I believe you all might be aware of some of the popular social networking sites such as Facebook, Twitter, Linkedin, Google+, and YouTube. Each of this site receive huge volume of data on a daily basis. It has been reported on some of the popular tech blocks that Facebook alone receives around 100 terabytes of data each day whereas Twitter processes around 400 million tweets each day, as far as LinkedIn and Google+ are concerned each of their site receives tens of terabytes of data on a daily basis and finally coming to YouTube it has been reported that each minute around 48 hours of flash videos are uploaded to YouTub. You can just imagine how much volume of data is being stored and processed on these sites. But as the number of users keep growing on these sites, storing and processing this data becomes a challenging task since this data holds a lot of valuable information. This data needs to be processed in a short span of time. By using this valuable information companies can boost their sales and generate more revenue. By making use of the traditional computing system we would not be able to accomplish this task within the given time frame as the computing resources of the traditional computing system would not be sufficient. For processing and storing such a huge volume of data this is where Hadoop comes into picture. Therefore we can term this huge volume of data as big data. Let me take another real-world example related to the airline industry and try to explain the term big data. For instance the aircraft's while they're flying they keep transmitting data to the air traffic control located at the airports. The air traffic control uses this data to track and monitor the status and progress of the flight on a real-time basis. Since multiple aircrafts would be transmitting this data simultaneously a huge volume of data gets accumulated at the air traffic control within a short span of time, therefore it becomes a challenging task to manage and process this huge volume of data using the traditional approach. Hence we can term this huge volume of data as big data. I hope you all might have understood what Big Data is.