In recent years, data visualization has become a crucial component of data analytics pipelines, enabling stakeholders to interpret complex datasets and make informed decisions. Tools such as Tableau have significantly simplified visualization tasks, while backend data processing approaches like SQL-based systems and Python-R analytics frameworks provide the computational foundation for transforming raw data into meaningful insights. This paper presents a comprehensive comparative study of SQL-based data processing and Python-R analytics for advanced data visualization using Tableau. The study evaluates both approaches in terms of performance, scalability, flexibility, and suitability for visualization-centric workflows. It also explores how datasets such as Netflix movies and IMDb datasets can be processed using both paradigms and visualized effectively in Tableau dashboards. The findings highlight that SQL-based processing is highly efficient for structured and large-scale querying tasks, while Python-R analytics provide superior flexibility for statistical modeling, machine learning integration, and advanced preprocessing. Tableau acts as a unifying visualization layer that bridges both approaches. The study concludes that hybrid pipelines combining SQL and Python-R provide the most effective solution for modern data-driven environments.

SQL-based data processing, Python analytics, R analytics, Tableau visualization

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