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Big Earth Datacube Analytics Made Easy

The BigDataCube project is developing flexible and scalable services for massive spatio-temporal Earth Observation (EO) data, offered as datacubes. This paradigm replaces the millions of EO files by a few massive multi-dimensional space/time objects, such as 3D image timeseries and 4D weather forecast cubes. This way, raster data get ready for spatio-temporal analysis in the large.

Concretely, the project deploys the European Datacube, rasdaman, in two infrastructures:
The commercial hosted processing environment of cloudeo. Novel datacube access control and quota will safely handle both free and proprietary data provided by Intermap and PlanetObserver.
The public service of CODE-DE, the German Copernicus hub, thereby complementing the batch-oriented Hadoop service with interactive extraction and processing along the paradigm of “any query, any time, on any size”. DLR will exemplarily establish a weather and ocean analytics tool based on rasdaman.

Further, CODE-DE and cloudeo services will be federated, allowing users to combine datacubes from both services without the need for downloading them first.

Goal of BigDataCube is to enhance access to value-adding services supporting collaboration across disciplinary and geographical boundaries for industry and research. The massively simplified Big Data handling benefits users of existing services as well as new businesses, e.g., in agro-informatics: they don’t need to develop or deploy complex technology and manage all data, but can use data readily, thereby freeing resources for their core business. Hence, on the BigDataCube platform novel, specialized services can be established by third parties in a fast, flexible, and scalable manner.
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