LIFE CYCLE OF DATA IN CLOUD

ABSTRACT Cloud computing has made a huge change in the big data management field. Cloud has come up with a technology that is flexible, scalable and it cuts down costs, thus making it possible to carry management and analysis of big data. The classic on-premises data management systems have always had a hard time handling the increasing amount of data that resembles big data in volume, variety, and velocity. Cloud environment like Microsoft Azure is capable of managing these huge datas with its robust infrastructure that allows companies to manage and work with large datasets with efficiency. By making use of such technologies, the organization can dynamically change the capacity of their working processors to suit the changing workload and be sure to use their resources to the maximum with the minimum cost. Another important advantage of making use of cloud computing technology in big data environments is that enterprises can exercise leverage on-demand resources. That is to say, organizations can swiftly increase or decrease the scale of their operations according to their dynamic needs, thus staying efficient. Nonetheless, in order to be completely effective in these assigning tasks to the cloud, one has to acquire the information regarding data transfer and processing which is vital for its proper functioning. This should involve knowledge of the data flow mechanisms, network latency, and the overall architecture of platforms like Azure. This research paper's primary focus has been on data processing and transportation in cloud environments, particularly on the Azure platform. It offers a thorough examination of the technological problems pertaining to the cloud ecosystem's data ingestion, processing, storage, and retrieval. It also examines security-related topics such data compliance, encryption, access management, and security standards in cloud systems. This clarifies the ways in which these factors impact data confidentiality and integrity in cloud-based big data management. Keywords—Cloud, Data Flow, Data lifecycle, Big Data