Parallel Computing Using Dask, Modin, and Ray
Modern data analysis increasingly involves working with datasets that are too large or too slow to handle efficiently with traditional workflows based on pandas and scikit-learn. Parallel computing allows users to decompose a data analysis task into smaller units and execute them across multiple CPU cores or even multiple machines. Instead of processing data row by row or file by file sequentially, we can distribute the work and combine the results, often achieving significant speedups and enabling the processing of data that no longer fits in memory.
Understanding parallel computing is essential for at least two reasons. First, real-world datasets continue to grow both in size and complexity. Second, production data workflows must often accommodate practical constraints, such as time windows, SLAs, and hardware limitations. The ability to design parallel pipelines will allow us to conduct analyses that are reasonably faster.
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