SciServer Compute: Bringing Analysis Close to the Data. SciServer Compute uses Jupyter notebooks running within server-side Docker containers attached to large relational databases and file storage to bring advanced analysis capabilities close to the data. SciServer Compute is a component of SciServer, a big-data infrastructure project developed at Johns Hopkins University that will provide a common environment for computational research. SciServer Compute integrates with large existing databases in the fields of astronomy, cosmology, turbulence, genomics, oceanography and materials science. These are accessible through the CasJobs service for direct SQL queries. SciServer Compute adds interactive server-side computational capabilities through notebooks in Python, R and MATLAB, an API for running asynchronous tasks, and a very large (hundreds of terabytes) scratch space for storing intermediate results. Science-ready results can be stored on a Dropbox-like service, SciDrive, for sharing with collaborators and dissemination to the public. Notebooks and batch jobs run inside Docker containers owned by the users. This provides security and isolation and allows flexible configuration of computational contexts through domain specific images and mounting of domain specific data sets. We present a demo that illustrates the capabilities of SciServer Compute: using Jupyter notebooks, performing analyses on data selections from diverse scientific fields, and running asynchronous jobs in a Docker container. The demo will highlight the data flow between file storage, database, and compute components.
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- Mattia Almansi, Renske Gelderloos, Thomas W. N. Haine, Atousa Saberi. Ali H. Siddiqui: OceanSpy: A Python package to facilitate ocean model data analysis and visualization (2019) not zbMATH