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Speed Up Your Data Processing: Parallel and Asynchronous Programming in Python
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Ong Chin Hwee is a data engineer, aspiring polymath and Industry 4.0 enthusiast who happens to be interested in things that fly (and stuff that burn to keep things flying). Hailing from a background in aerospace engineering and computational modelling, Chin Hwee has experience working on innovative projects in collaboration with academia and industry partners. Chin Hwee's latest project involves contributing to the documentation for pandas, an open-source Python library that is immensely useful for anything related to data.
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(Apologies for the abrupt cut)
In a data science project, one of the biggest bottlenecks (in terms of time) is the constant wait for the data processing code to finish executing. Slow code, as well as connectivity issues, affect every step of a typical data science workflow — be it for event-driven I/O operations or computation-driven workloads. Through real-life analogies based on my experience in a young data science team getting started with real-world data, I will be exploring the use of parallel and asynchronous programming in Python to speed up your data processing pipelines so that you could focus more on getting value out of your data.
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