The purpose of this talk is to provide a comprehensive state of the art concerning the evolution of data management systems from uniprocessor file systems to cloud systems. In the landscape of database management systems, data analysis systems (OLAP) and transaction processing systems (OLTP) are separately managed. The reasons for this dichotomy is that both systems have very different functionalities, characteristics and requirements. My talk will focus on the first class of OLAP systems. In this perspective, mainly, data management based on parallel and cloud (e.g. MapReduce) systems are overviewed, and compared by relying on meeting software requirements (e.g. data independence,  software reuse), high performance, performance isolation, availability, scalability, and elasticity. With respect to proposed solutions, we try to learn some lessons and point out some open issues  that should be tackled to ensure the viability of the next generation of large-scale data management systems.

Key words:

Data Management, Data partitioning Query Processing and Optimization, Resource Allocation, Parallel Relational and Distributed Database Systems, Data Grid Systems, Data Cloud Systems, MapReduce, Cost Models, Scalability, Elasticity.