Histogramming is a fundamental operation in scientific data analysis, but as datasets grow and computational demands increase, traditional approaches can become bottlenecks, especially in high-performance computing (HPC) environments. YODA (Yet Another Object-Oriented Data Analysis) addresses this challenge by providing a lightweight, C++-based histogramming library optimised for HPC use cases. In this talk, we’ll delve into YODA’s design principles and its approach to memory efficiency and parallel processing. We’ll discuss how YODA’s architecture supports large-scale histogramming workflows in data-intensive fields, with particular focus on LHC data analysis applications. Through examples, we’ll demonstrate YODA’s ability to handle high-throughput demands, leveraging modern C++ features to ensure compatibility with HPC and GPU architectures. This session will be of interest to developers and researchers working in high-performance data analysis who seek efficient, open-source solutions for handling complex datasets in resource-intensive environments.