Modern organizations operate in highly data-driven environments where decision-making depends on the ability to process large, heterogeneous, and fast-moving datasets. Traditional single-technology systems are insufficient to handle the complexity of Big Data workloads due to limitations in scalability, flexibility, and integration. This paper proposes an integrated data analytics framework combining SQL, Python, R, and Tableau to enable scalable Business Intelligence (BI) systems. The framework integrates distributed storage systems, SQL-based query engines, advanced statistical and machine learning tools, and interactive visualization platforms into a unified architecture. The objective is to support end-to-end data processing pipelines that transform raw data into actionable insights. The paper also discusses enabling technologies such as Hadoop, Spark, polystore systems, and in-memory computing platforms, along with real-world applications in healthcare, agriculture, and e-commerce domains. The proposed framework addresses key Big Data challenges including volume, velocity, variety, and heterogeneity while improving efficiency and decision-making capabilities.

Big Data Analytics, Business Intelligence (BI), SQL, Python

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