In the era of big data, organizations increasingly rely on data analytics to support both operational and strategic decision-making processes. This research presents a comprehensive framework for analyzing retail sales data using Python, SQL, Microsoft Excel, and Power BI. The study focuses on transforming raw transactional data into actionable insights through systematic data preprocessing, storage, querying, and visualization. Python is utilized for data cleaning and transformation, SQL for efficient database management and structured querying, Excel for initial preprocessing, and Power BI for developing interactive dashboards. Key performance indicators such as total revenue, order count, average order value, category-wise distribution, and salesperson performance are evaluated to understand business trends. The results reveal significant patterns in sales performance, highlight dominant product categories, and identify variations in salesperson contribution. The proposed integrated approach demonstrates improved analytical efficiency, enhanced data interpretation, and effective support for data-driven decision-making in retail business environments.