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Making Research Data Flow with Python

Description

Many tools exist for large-scale data transfer (tens of terabytes or more), but they often don't match the needs of scientific data flows. In this talk, I'll explain how we built the 'librarian' framework with FastAPI, postgres, and Globus to ease this challenge. Designed for the Simons Observatory's petabyte-scale data transfer, I'll cover building reliable web services, flexible development with dependency injection, effective testing with pytest, and deployment using NERSC's Spin. I hope to demystify web and database programming for a scientific audience.

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