Evaluating Several Design Patterns and Trends in Big Data Warehousing Systems

The Big Data characteristics, namely volume, variety and velocity, currently highlight the severe limitations of traditional Data Warehouses (DWs). Their strict relational model, costly scalability, and, sometimes, inefficient performance open the way for emerging techniques and technologies. Recently, the concept of Big Data Warehousing is gaining attraction, aiming to study and propose new ways of dealing with the Big Data challenges in Data Warehousing contexts. The Big Data Warehouse (BDW) can be seen as a flexible, scalable and highly performant system that uses Big Data techniques and technologies to support mixed and complex analytical workloads (e.g., streaming analysis, ad hoc querying, data visualization, data mining, simulations) in several emerging contexts like Smart Cities and Industries 4.0. However, due to the almost embryonic state of this topic, the ambiguity of the constructs and the lack of common approaches still prevails. In this paper, we discuss and evaluate some design patterns and trends in Big Data Warehousing systems, including data modelling techniques (e.g., star schemas, flat tables, nested structures) and some streaming considerations for BDWs (e.g., Hive vs. NoSQL databases), aiming to foster and align future research, and to help practitioners in this area.