Abstract
Big Data triggered furthered an influx of research and prospective on concepts and processes pertaining previously to the Data Warehouse field. Some conclude that Data Warehouse as such will disappear; others present Big Data as the natural Data Warehouse evolution (perhaps without identifying a clear division between the two); and finally, some others pose a future of convergence, partially exploring the possible integration of both. In this paper, we revise the underlying technological features of Big Data and Data Warehouse, highlighting their differences and areas of convergence. Even when some differences exist, both technologies could (and should) be integrated because they both aim at the same purpose: data exploration and decision making support. We explore some convergence strategies, based on the common elements in both technologies. We present a revision of the state-of-the-art in integration proposals from the point of view of the purpose, methodology, architecture and underlying technology, highlighting the common elements that support both technologies that may serve as a starting point for full integration and we propose a proposal of integration between the two technologies.
Highlights
Information is one of the most valuable resources of an institution, and adequate use to support decision making has become a challenge of ever increasing complexity
With the raise of Big Data, it could be speculated that Data Warehouse has already met the apex of its life cycle
As it was exposed in this work, Data Warehouse (DW) and Big Data (BD) are complementary and could be integrated to share data, and storage and processing computational resources
Summary
Information is one of the most valuable resources of an institution, and adequate use to support decision making has become a challenge of ever increasing complexity. Nowadays the data volume required to be processed within an enterprise can reach the order of the Exabyte [1] This poses storage and processing challenges that require new technological solutions that allow storage, and updating, efficient exploitation and that have into account data requirements. This is sometimes referred as the seven Vs [1]: Volume, Variety, Velocity, Veracity, Value, Variability and Viability and the three Cs [1]: Cost, Complexity and Consistency. As a result of the art state review, we can conclude that some articles present Big Data as the Data Warehouse replacement, others as Data Warehouse evolution [5], some propose the extension of Data Warehouse to support some Big Data characteristics and others partially explore the possibility of integrating the two
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