Abstract
The use of Jupyter notebooks as a platform for data management is becoming popular in different disciplines thanks to the features provided: web-based interface, code running capabilities, customization, kernel configuration, etc. However, it is sometimes limited to the server capacity, so working with big datasets is difficult since certain data analysis requires intensive computing or even GPUs. Thanks to diverse Cloud Computing-based solutions, especially those provided by eXtreme-DataCloud project, these limitations can be solved, providing an integrated environment where storage and computing resources could support a system in a transparent way for the user.
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