Meeting the challenges that are presented by the big data has become one of the biggest concerns for the organizations. The volume of data is previously enormous and is continually growing with the passage of time. The swiftness of its growth and generation is increasing, driven in part due to the propagation of the internet connected devices and applications. In addition to it, the variety of data has also been produced that is also expanding with the passage of time. However, the capability of the organization to apprehend and process the data is limited. Therefore, it can be stated that the organizations are facing considerable issues and challenges, while using big data within the cloud technology. In order to present more cohesive understanding, the proceeding paper includes the analysis of some of those challenges.
When the information is consumed by human, a prodigious deal of the heterogeneity has been securely abided. Indeed, the degree and richness of the natural language can also provide appreciated depth. Machine analysis algorithm, however, expects homogeneous data, and is unable to interpret nuance. Significantly, data should be structured prudently as an initial step in the data analysis.
The first thing that comes in the mind regarding the big data is its size. Managing large and quickly cumulative data volume has become challenging concern for the organizations, since last few decades. In the previous era, these challenges were alleviated by due to the utilization of fast speed processors that were based on Moore’s law. The main objective of using those processors was to provide the means that are usually required to cope with increasing data volumes. However, there exist a fundamental shift proceeding recently; data volume is ascending quicker than that of the computer resources, as well as the speed of the CPU are stationary.
One of the dramatic ways that is widely being used is to move towards the cloud computing technology. Cloud technology has now been aggregated in multiple workloads along erratic performance goals into prodigious clusters. This level of resource sharing upon large and expensive clusters needs new ways for the determination of the running and executing the data processing tasks Moreover, cloud technology also helps in dealing with the system failure that appears in a frequent manner, as it usually operates upon grander data clusters. Reliance upon the optimizations of the user-driven program is likely to lead towards the underprivileged cluster utilization; reason is that the user is usually unaware regarding the program of the other users. System-driven holistic optimization needs programs to be significantly transparent, which includes; relational database systems in which, the declarative query languages are premeditated in accordance with the respective technology.
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