Snap processes more than 10 petabytes of experimentation data every single morning—and with NVIDIA GPU-accelerated Apache Spark on Google Cloud, Snap cut job costs by 76%, reduced memory usage by 80%, and eliminated 120 terabytes of disk spill from its pipelines.Prudhvi Vatala, head of engineering platforms at Snap, joins the NVIDIA AI Podcast to break down how he and his team completely modernized data infrastructure for a social platform serving nearly a billion monthly active users—using NVIDIA cuDF plugin (formerly referred to as NVIDIA RAPIDS plugin) for Apache Spark on Google Kubernetes Engine, with zero application code changes.🔬Topics covered:How Snap runs A/B tests at planetary scale using rigorous statistical methods like heterogeneous treatment effect detection and variance reductionWhy Snap reuses idle inference GPUs between 1–5 a.m. for batch data processing—and how it built a Kubernetes-based platform to do itHow NVIDIA cuDF delivered 3x+ speedups on join-heavy Spark jobs with no code rewritesThe full business impact: 76% cost reduction, 62% fewer cores, 80% less memory, 120 TB of spill eliminatedHow a three-way partnership between Snap, NVIDIA, and Google Cloud made it possible in just 8–9 monthsChapters:Learn more:NVIDIA cuDF: https://developer.nvidia.com/topics/ai/data-science/cuda-x-data-science-libraries/cudf#accel-apache