Big Data Management

The architecture of Big Data must be synchronized with the support infrastructure of the organization. To date, all of the data used by organizations are stagnant. Data is increasingly sourced from various fields that are disorganized and messy, such as information from machines or sensors and large sources of public and private data. Previously, most companies were unable to either capture or store these data, and available tools could not manage the data in a reasonable amount of time. However, the new Big Data technology improves performance, facilitates innovation in the products and services of business models, and provides decision-making support. Big Data technology aims to minimize hardware and processing costs and to verify the value of Big Data before committing significant company resources. Properly managed Big Data are accessible, reliable, secure, and manageable. Hence, Big Data applications can be applied in various complex scientific disciplines (either single or interdisciplinary), including atmospheric science, astronomy, medicine, biology, genomics, and biogeochemistry. In the following section, we briefly discuss data management tools and propose a new data life cycle that uses the technologies and terminologies of Big Data.