Thanks for the feedback! You're right that for scientific use cases, the data access story defines mode and methods of computation. Your use case of having a

Thanks for the feedback! You're right that for scientific use cases, the data access story defines mode and methods of computation. Your use case of having a large dataset stored across many files on a distributed FS is one of the cornerstone motivations for building Blaze, next-generation distributed Numpy: https://speakerdeck.com/sdiehl/blaze-next-generation-numpy https://speakerdeck.com/sdiehl/blaze-next-generation-numpy http://vimeopro.com/continuumanalytics/pydata-nyc-2012 http://vimeopro.com/continuumanalytics/pydata-nyc-2012 Rather than traditional "load all the data into memory" sort of approaches, Blaze is inherently out-of-core, and allows the user to define mappings of an index space onto local or remote files, and then manipulate that structure at the Python prompt as easily as if it were a small matrix stored in memory. There are existing PGAS approaches that are similar in spirit, but they tend to invoke heavyweight MPI machinery or make assumptions about the regularity or structure of the dataset that is being distributed across the cluster. Our guidance in designing and implementing Blaze is to make simple distributed things easy, and hard things possible; we are not trying to solve decades of distributed computing and linear algebra problems as a start-up. :-) So, hopefully you can see that with Blaze as your data access mechanism, and Wakari as a web-based front-end, you should be able to do large scale compute from within a web browser.