Big Data, as the name suggests, deals with an astronomical amount of data that many organizations collect these days. Because of the fact that such data sets and technology stacks are immensely complex and large, it becomes difficult to not just capture and store data but also share, analyze and visualize it. In fact, such data is at times beyond the processing capability of conventional tools. With decision making and deriving of analysis being difficult with such huge data size, it leads to a massive loss of business opportunity for the organization. It is, hence, that Big Data solutions are availed which make it conducive and even affordable to perform computations on a large volume of data. Hence, resources like web server log files, sensor logs, content of social media, photographic archives and others are computed with ease and that leads to better decision making.
With the volume and velocity of data with organizations having assumed colossal proportions, one requires efficient frameworks that can be utilized to manage big data. Hadoop is one such software which proposes reliable & scalable computation of data sets. It can be of immense value to those who have their network of operations spread across the geography of the globe. This is because Hadoop can facilitate distributed processing of big data. Such feat can be achieved by just making use of basic programming models and given that.
MongoDB Different solutions have been proposed in the recent times to not just accommodate the enormous data that organizations capture but also derive knowledge from the same. With traditional databases proving to be incompetent in managing such massive data size, newer and more robust solutions are being sought and MongoDB fits the bill quite smartly. Written in C++ language, this document oriented database offers benefits that can transform the way in which an organization’s big data is managed. Given that hosting large enterprise data would require a powerful database, MongoDB brings