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Speed Up Your Data Processing: Parallel and Asynchronous Programming in Python - Chin Hwee Ong
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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.
FOSSASIA Summit 2020 - Python
Speaker: Chin Hwee Ong, Data Engineer ST Engineering
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.
FOSSASIA Summit 2020 - Python
Speaker: Chin Hwee Ong, Data Engineer ST Engineering
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