The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. In this context, emerging innovative solutions exploit several underlying infrastructure and cluster management systems. However, these systems have not been designed and implemented in a 'big data context', and they rather emphasize and address the computational needs and aspects of applications and services to be deployed. In this paper we present the architecture of a complete stack (namely BigDataStack), based on a frontrunner infrastructure management system that drives decisions according to data aspects, thus being fully scalable, runtime adaptable and high-performant to address the needs of big data operations and data-intensive applications. Furthermore, the stack goes beyond purely infrastructure elements by introducing techniques for dimensioning big data applications, modelling and analyzing of processes as well as provisioning data-as-a-service by exploiting a seamless analytics framework.