<ul><li>As datasets continue growing exponentially in 2025, data scientists are facing challenges with their Jupyter Notebooks.</li><li>Modern tools and techniques such as Dask and Vaex can optimize big data workflows by processing data in parallel and memory-mapping.</li><li>The RAPIDS suite, with cuDF and cuML libraries, provides GPU-accelerated operations for faster processing and machine learning tasks.</li><li>Major cloud providers have enhanced Jupyter integration, offering high-RAM instances and auto-scaling to handle large datasets and heavy computations.</li></ul>

Optimizing Jupyter Notebooks for Big Data in 2025: A Practical Guide

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