The success stories of the database technology keep companies continuously demanding more and more efficient services related to two principal entities: data and queries. The services dedicated to data concern mainly the following tasks: collecting, cleaning, filtering, integration, sharing, storing, transferring, visualizing, analyzing, securing. Regarding queries, services are often concentrated on processing, optimizing, personalizing and recommending. These services of both entities have to be revisited and extended to deal with the dimensions brought by Big Data Era and the new requirements of companies in the contexts of globalization, competition and climate change. While (Huang et al. 2017) point out the seven V’s of Big Data, we would like to highlight four dimensions characterized by four V’s of Big Data, that challenges the traditional solutions of databases, in terms of data acquisition, storage, management and analysis. The first V concerns the Volume of data generated by traditional and new providers. To illustrate the data deluge, let consider three examples of data providers:1 (i) the massive use of sensors (e.g. 10 Terabyte of data are generated by planes every 30 minutes), (ii) the massive use of social networks (e.g., 340 million tweets per day), (iii) transactions (Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 Peta-bytes of data). The second V is associated to the Variety, where data may come from various data sources, in different formats such as transactions, log data, social network, sensors, etc. from various applications, structured data as database table, semistructured data such as XML data, unstructured data such as text, images, video streams, audio statement, and more. The third V is about the Velocity, where large amounts of data from transactions with high refresh rate resulting in data streams coming at great speed. The time to act on the basis of these data streams will often be very short. Consequently, the traditional batch processing has to move to real time streaming. The fourth V that got little attention is related to Vocabulary used to describe schemes, data models, semantics, ontologies, taxonomies, and other content- and context-based metadata that describe the data’s structure, syntax, content, and provenance. The traditional database non-functional requirements were mainly focused on (i) improving low-latency query processing to satisfy the needs of end users (e.g., decision makers), (ii) minimizing the maintenance cost of the databases and optimization structures (e.g. indexes and materialized views) and (iii) better usage of the storage cost dedicated to store the optimization structures have been considered. These requirements have been enriched by new ones; mainly motivated by the development of green computing and services delivered by Cloud Computing. This for some years now, the international community regrouping states, governments, associations, and users has been closely involved in climate change by proposing initiatives to limit global warming. The database community has spared no effort propose initiatives in this sense. As a consequence, the reduction of energy has become a new non-functional requirement integrated in the processes of design and the exploitation of database and information systems (Roukh et al. 2017).