Most data pipelines don’t need more infrastructure. They need less overhead. I recently benchmarked a 10M+ row ingestion task on a standard machine to test the "Abstraction Tax" of modern data libraries: Pandas Baseline: 7.75s Custom C-Engine (Axiom): 0.31s That is a 24.5x improvement on the exact same hardware. This isn't magic; it's simply removing the layers between the code and the hardware. The Problem: The High Cost of "Convenience" Industry standards like Pandas and NumPy are phenomenal for developer convenience, but in high-entropy environments (trading, log parsing, real-time analytics), that convenience carries a massive cost: Slow Ingestion: Seconds of idle time per run. Memory Overhead: Massive RAM spikes due to redundant object copies. Scaling Costs: Throwing more AWS/Azure compute at inefficient code. The Baseline: Why is it Slow? Standard Python ingestion is slow because it’s generalized.…