A Proposal for a Reference Architecture for Long-Term Archiving, Preservation, and Retrieval of Big Data
Phillip Luiz Viana, Líria Matsumoto Sato · 2014
The volume of data stored in corporate data centers has been growing at a rate of 35% to 50% per year [1]. The exponential growth in data volume leads to some challenges from the technical, operational and financial perspectives. Along with this increase in the data volume the demand for preservation (or retention) of such data has also increased due to government regulations. The convergence of these two trends (growth of data volume and increased demand for preservation) implies that storage systems must support the preservation of data for very long periods of time. Several studies address the archiving, preservation and retrieval of structured data. To the best of our knowledge, there couldn't be found reference architectures specifically focused on the archiving, preservation and retrieval of both unstructured and structured data. Our research goal is to propose a reference architecture for the long term archiving, preservation and retrieval of Big Data.