The problem of sharing big numpy arrays between parent and child processes in Python is addressed in this guide. In order to maximize resource use and prevent data duplication, it highlights the utilization of shared memory. Real-world examples, such working with HDF5 files, show how to use these strategies in real-world situations, improving scalability and performance.