The consortium of this European Training Network (ETN) “BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data” will train future data scientists in order to enable them and us to apply holistic and interdisciplinary approaches for taking advantage of a data-overwhelmed world, which requires HPC and Cloud infrastructures with a redefinition of storage architectures underpinning them – focusing on meeting highly ambitious performance and energy usage objectives.
There has been an explosion of digital data, which is changing our knowledge about the world. The drivers for this data deluge are twofold: the interest of enterprises and agencies in collecting, processing, and publishing heterogeneous data, derived from multiple sources (e.g. sensors, scientific experiments) as well as citizens publishing content through channels such as social networks and Cloud systems. To gain value from this data it must be analysed and often combined or compared with simulated and predicted data. This huge data collection, which cannot be managed by current data management systems, is known as Big Data. Techniques to address it are gradually combining with what has been traditionally known as High Performance Computing. Therefore, this ETN will focus on the convergence of Big Data, HPC, and Cloud data storage, its management and analysis.
A number of initiatives and studies, such as PRACE and ETP4HPC , have emphasized the lack of professionals with the skills to address the EC goals to provide Europe with the necessary ecosystem of technology providers, research infrastructures, and application developers in HPC, Cloud, Storage, Energy, or Big Data to maintain Europe’s economy. Moreover, these reports also address the need of an interdisciplinary training, which enables the efficient interaction between application-domain, numerical analysis and computer-science in the HPC field.
To gain value from Big Data it must be addressed from many different angles: (i) applications, which can exploit this data, (ii) middleware, operating in the cloud and HPC environments, and (iii) infrastructure, which provides the Storage, and Computing capable of handling it.
Big Data can only be effectively exploited if techniques and algorithms are available, which help to understand its content, so that it can be processed by decision-making models. This is the main goal of Data Science, a new discipline related to Big Data that incorporates theories and tools from many areas, including statistics, machine learning, visualization, databases, or highly parallelised HPC programming.
We claim that this ETN project will be the ideal means to educate new researchers on the different facets of Data Science (across storage hardware and software architectures, large-scale distributed systems, data management services, data analysis, machine learning, decision making). Such a multifaceted expertise is mandatory to enable researchers to propose appropriate answers to applications requirements, while leveraging advanced data storage solutions unifying cloud and HPC storage facilities.
