Building Data Warehouses in the Era of Big Data An Approach for Scalable and Flexible Big Data Warehouses

During the last few years, the concept of Big Data Warehousing gained significant attention from the scientific community, highlighting the need to make design changes to the traditional Data Warehouse (DW) due to its limitations, in order to achieve new characteristics relevant in Big Data contexts (e.g., scalability on commodity hardware, real-time performance, and flexible storage). The state-of-the-art in Big Data Warehousing reflects the young age of the concept, as well as ambiguity and the lack of common approaches to build Big Data Warehouses (BDWs). Consequently, an approach to design and implement these complex systems is of major relevance to business analytics researchers and practitioners. In this tutorial, the design and implementation of BDWs is targeted, in order to present a general approach that researchers and practitioners can follow in their Big Data Warehousing projects, exploring several demonstration cases focusing on system design and data modelling examples in areas like smart cities, retail, finance, manufacturing, among others.