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Explore GPU-accelerated graph analytics through this 49-minute conference talk that introduces GFQL, the first open source dataframe-native graph query language designed for massive scale and seamless Python integration. Learn how GFQL harnesses GPU acceleration to achieve up to 42X speedups on real-world graphs and discover its practical applications across billion-dollar lawsuits, cybersecurity incident response, clickstream analytics, and real-time Bluesky firehose mining. Understand the evolving landscape of graph computing as LLMs and event data make graphs increasingly attractive for answering fundamental questions about unique entities, their activities, and relationships. Discover how GFQL eliminates the overhead of external graph databases, allowing you to work directly from Jupyter notebooks, Python dashboards, and web apps with a simple pip install. Master the integration of GFQL with PyData, Arrow, and GPU libraries for comprehensive end-to-end graph analytics, from data loading to interactive visualization and AI applications, while exploring the design principles and usage patterns that make this tool effective for modern data analysis challenges.
