Have you ever experienced the frustration of not being able to analyze a dataset because it's too large to fit in memory? Or perhaps you've encountered the memory wall, where computation is hindered by slow memory access? In this hands-on tutorial, you'll learn how to overcome these common challenges using Python-Blosc2.

Python-Blosc2 (https://www.blosc.org/python-blosc2/) is a high-performance, multi-threaded, multi-codec array container, with an integrated compute engine that allows you to compress and compute on large datasets efficiently. You'll gain practical experience with Python-Blosc2's latest features, including its seamless integration with NumPy and the broader Python data ecosystem. Through guided exercises, you'll discover how to tackle data challenges that exceed your available RAM while maintaining high performance.

By the end of this tutorial, you'll be able to implement Python-Blosc2 in your own workflows, dramatically increasing your ability to process large datasets on standard hardware. Participants should have basic familiarity with NumPy and Python data processing.

Have you ever experienced the frustration of not being able to analyze a dataset because it's too large to fit in memory? Or perhaps you've encountered the memory wall, where computation is hindered by slow memory access? These are common challenges in data science and high-performance computing.

Python-Blosc2 (https://www.blosc.org/python-blosc2/) is a high-performance, multi-threaded, multi-codec array container, with an integrated compute engine that allows you to compress and compute on large datasets efficiently. In this talk, we will explore the latest features of Python-Blosc2, including its seamless integration with NumPy, and the Python Data ecosystem in general, and how it can help you tackle data challenges that exceed the limits of your available RAM, all while maintaining high performance.