Python is the king of data science, but it charges a heavy price for convenience. When you use pd.read_csv() on a 10GB+ file, Python attempts to load the data into RAM, wrapping every byte in a heavy PyObject. The result? OOM (Out of Memory) crashes and massive AWS bills. I decided to go to the metal to see if I could bypass this "Abstraction Tax" entirely. The Problem: The Double-Copy Penalty Standard data pipelines move data from the SSD ➔ OS Kernel ➔ User Space ➔ Application. This constant copying wastes CPU cycles and explodes the memory footprint. The Solution: Memory Mapping (mmap) I built the Axiom Zero-RAM Extractor in pure C. Instead of loading the file, Axiom uses mmap to treat the SSD as a direct array. Key Architectural Gains: Zero-Copy: Data is only pulled into the L1/L2 cache in tiny 4KB chunks as the CPU requests them. Mechanical Sympathy: Sequential access triggers the CPU's Hardware Pre-fetcher, hitting the physical read limit of the NVMe drive.…