Conclusion

This paper presents the fundamental concepts of Big Data. These concepts include the increase in data, the progressive demand for HDDs, and the role of Big Data in the current environment of enterprise and technology. To enhance the efficiency of data management, we have devised a data-life cycle that uses the technologies and terminologies of Big Data. The stages in this life cycle include collection, filtering, analysis, storage, publication, retrieval, and discovery. All these stages (collectively) convert raw data to published data as a significant aspect in the management of scientific data. Organizations often face teething troubles with respect to creating, managing, and manipulating the rapid influx of information in large datasets. Given the increase in data volume, data sources have increased in terms of size and variety. Data are also generated in different formats (unstructured and/or semistructured), which adversely affect data analysis, management, and storage. This variation in data is accompanied by complexity and the development of additional means of data acquisition.

The extraction of valuable data from large influx of information is a critical issue in Big Data. Qualifying and validating all of the items in Big Data are impractical; hence, new approaches must be developed. From a security perspective, the major concerns of Big Data are privacy, integrity, availability, and confidentiality with respect to outsourced data. Large amounts of data are stored in cloud platforms. However, customers cannot physically check the outsourced data. Thus, data integrity is jeopardized. Given the lack of data support caused by remote access and the lack of information regarding internal storage, integrity assessment is difficult. Big Data involves large systems, profits, and challenges. Therefore, additional research is necessary to improve the efficiency of integrity evaluation online, as well as the display, analysis, and storage of Big Data.