Big Data Augmentation with Data Warehouse: A Survey

With dynamic changes in world's technology, an increasing growth and adoption observed in the usage of social media, computer networks, internet of things, and cloud computing. Research experiments are also generating huge amount of data which are to be collected, managed and analyzed. This huge data is known as "Big Data". Research analysts have perceived an increase in data that contains both useful and useless entities. In extraction of useful information, data warehouse finds difficulties in enduring with increasing amount of data generated. With shifts in paradigm, big data analytics emerged as promising area of research which supports business intelligence in terms of decision making. This paper provides a comprehensive survey on BigData, BigData problems, BigData Analytics and Big Data Warehouse. In addition, it also explains how the need for augmentation of big data and data warehouse emerged in perspective of decision making, comparing methods and research problems. It also elaborates applications which support Big Data, Data Warehouse, and its challenges.

I. Introduction

If we start by looking at the historical perspective of the growth cycle of data, realizing the pattern how trend changes in recent times e.g. 1) Relational databases, 2) Online Analytical Processing (OLAP), 3) Column based data storage and cloud computing, and 4) Big data applications [1]. A need arises for handling high data streaming. Initially, it was addressed by data management/storage based systems. In recent years, the rate of data streaming is increasing which is difficult to handle [2].