Julia in Scientific Computing: Efficiency Analysis versus Python and R in Big Data Environments

Abstract

This article presents a comparative study of Julia's efficiency versus Python and R in scientific computing under Big Data scenarios. Execution times, memory consumption, and scalability are evaluated in linear regression tasks, Monte Carlo simulations, and matrix factorization, both on a single node and in distributed environments. The results, based on evidence reported in academic literature and reproducible with the attached code, show that Julia achieves substantial advantages in intensive computing and vectorization, while Python stands out for the maturity of its ecosystem (NumPy, Dask, PySpark) and R maintains strength in advanced statistical analysis.