Amar Nath Singh¹, Rakesh Kumar Rath^1,

In Database the prime objective of database is to arrange the data in a well designed manner so that the data should be stored and retrieved effectively as and when required. But in traditional database like DBMS the major fault was scalability. So in this database management systems (DBMS) the following problems are arise always such as, 1. Update intensive application workloads, and 2. Decision support systems for descriptive and deep analytics. These are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. So these scalability natures of the data base cause a lot of problem for data integration when the amounts of data were huge. This paper presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. We have designed this paper on the basis of the following aspects, such as: (i) For supporting update heavy applications, and (ii) For ad-hoc analytics and decision support. Here we also focus on providing an in-depth analysis of systems for supporting update intensive web-applications which are mostly now a day’s used in the companies and provide a survey of the state-of-the art in this domain. We have tried to crystallize the design aspect of choices made by considering some large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.

cloud, big data, crystallization, data base, scalable data