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.…