With the increase in the amount of data present over the cloud, there is a need for an efficient management of data to research and industry. Big Data is used by different organizations to extract valuable information which can be analyzed computationally to reveal trends, patterns, and associations exposing the human interaction and behavior for making various industrial decisions. Due to the enormous volume of data, the traditional systems are becoming incapable of storing and computing such voluminous data. To resolve this issue, the data is stored in the cloud, and all the analysis is done over Big Data using the cloud. But to make any practical decision, the data must be optimized, secured and visualized. Analysing large volume of data is not beneficial always unless it is adequately investigated. A perfect knowledge base should be selected. The techniques which are available right now are insufficient to analyze the Big Data and identify the frequent services accessed by the cloud users. Various functions can be integrated to provide a better environment to work in. Using these services, people become widely vulnerable to exposure. That is, it becomes possible to collect more data than it is required which may lead to leakage of the data and hence security concerns are at stake. Results can be analyzed in a better way by visuals like graphs, charts, etc. and thus, helps in faster and efficient decision-making and predictive modeling which can further extend this domain to Artificial Intelligence. MapReduce Algorithm assists in maintaining a log of user’s activities in the cloud and show the frequently used services. This paper shows the advancements done in the field of Data Analytics with Cloud Computing and Big Data, and also proposes a scheme for making Big Data Analytics more accurate, efficient and beneficial to the Cloud environment.

Big Data Analytics; Integration; Cloud Computing; Privacy; Artificial Intelligence