How do you handle large datasets in Python without compromising speed?
Handling large datasets efficiently in Python is crucial for data engineering. The key is to process data in a way that minimizes memory usage and maximizes speed. Python, with its rich set of libraries and tools, can handle big data if you know the right techniques. Understanding how to work with large datasets while maintaining performance can save you from running into bottlenecks that slow down your data processing tasks. Let's dive into some strategies that can help you manage big data with ease.
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Alestan AlvesData Engineering Coordinator at TOTVS | AI Engineer | Creator at @ackercode 300k+ followers
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Simon NgugiDATA ENGINEER||ANALYTICS ENGINEER |||Transforming data to business value Created a data engineering community…
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Harsha MathanPrincipal Data Engineer at Verisk | AWS Community Builder