Abstract
Data Science has subsumed Big Data Analytics as an interdisciplinary endeavor, where the analyst uses diverse programming languages, libraries and tools to integrate, explore and build mathematical models on data, in a broad sense. Nowadays, there exist many systems and approaches, which enable analysis on practically any kind of data: big or small, unstructured or structured, static or streaming, and so on. In this survey paper, we present the state of the art comparing the strengths and weaknesses of the most popular languages used today: Python, R and SQL. We attempt to provide a thorough overview: we cover all processing aspects going from data pre-processing and integration to final model deployment. We consider ease of programming, flexibility, speed, memory limitations, ACID properties and parallel processing. We provide a unifying view of data storage mechanisms, data processing algorithms, external algorithms, memory management and optimizations used and adapted across multiple systems.
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Ordonez, C. (2020). A Comparison of Data Science Systems. In: Bellatreche, L., Goyal, V., Fujita, H., Mondal, A., Reddy, P.K. (eds) Big Data Analytics. BDA 2020. Lecture Notes in Computer Science(), vol 12581. Springer, Cham. https://doi.org/10.1007/978-3-030-66665-1_1
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DOI: https://doi.org/10.1007/978-3-030-66665-1_1
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