Data volumes and streaming rates are expanding. In particle physics and astronomy, exa-scale data volumes present huge challenges for processing and storage. In the life sciences, low-cost genome sequencing and advanced imaging technologies also push the limits of conventional data processing, analysis, transfer and storage. New technologies, systems and infrastructure must be developed in order to handle these data volumes. 

Big Data is also complex and heterogeneous, and the possibility to link and query heterogeneous datasets offers a great opportunity to develop knowledge unattainable by studying individual datasets in isolation. However, proper schemes to manage and store data, and the proper management of metadata, including data on sample preparation, experimental parameters, and the data's provenance, is essential to enable Big Data to deliver trustworthy results. 

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