How I Handled 100GB Datasets in Python Without Crashing My System

How I Built a Zero-Copy Data Pipeline in Python to Handle 100GB Datasets (Without Crashing RAM) If you have ever worked with large-scale data, you know the exact feeling of dread when your terminal pauses for three minutes, only to spit out a fatal MemoryError . As a Computer Science Master's student exploring high-performance systems and neuroinformatics, I ran into this problem immediately. Modern computational neuroscience generates massive amounts of data. A single Allen Neuropixels probe can easily produce gigabytes of high-frequency (30kHz) binary data. If you want to temporally align that brain data with a 60 FPS behavioral video and a BIDS-compliant fMRI scan, standard procedural data loaders will max out your hardware and crash your pipeline. To solve this, I built NeuroAlign : an open-source, object-oriented Python library that uses OS-level memory mapping to load, filter, and mathematically synchronize out-of-core multimodal datasets. Here is a deep dive into the architectur