🚀 Axiom Protocol v1.1: High-Performance Data Ingestion I architected the Axiom Protocol to solve...🚀 Axiom Protocol v1.1: High-Performance Data Ingestion I architected the Axiom Protocol to solve...
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🚀 Axiom Protocol v1.1: High-Performance Data Ingestion
I architected the Axiom Protocol to solve a critical bottleneck in large-scale data pipelines: the high latency and precision drift inherent in standard high-level ingestion libraries.
By engineering a surgical C-extension and leveraging zero-copy memory mapping (mmap), I bypassed the CPython abstraction tax to deliver a system optimized for both speed and deterministic accuracy.
Key Achievements:
Performance: Achieved a 2.8x speedup over industry-standard Pandas (reducing ingestion from 7.75s to 2.7s for 10M rows).
Infrastructure ROI: Reclaimed ~14 hours of CPU compute daily at an enterprise scale of 10,000 files processed.
Precision: Eliminated silent data corruption and IEEE 754 floating-point drift using custom integer accumulation logic.
This project serves as a benchmark for my approach to Systems Architecture: reducing cloud overhead by optimizing software at the metal layer.
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