DQLab Store Sales Performance with Spark SQL

Analyzing transactions, promotions, and customer trends In this project, I explore DQLab Store’s transaction data (2009–2012) using Spark SQL to uncover insights into overall sales performance, promotion effectiveness, and customer behavior. With over 5,500 rows of order details — including order status, quantity, discounts, and product categories — this analysis demonstrates how Spark SQL can transform raw data into actionable business intelligence. Highlights of the video: - Step-by-step sales performance analysis using Spark SQL queries - Insights into finished, returned, and cancelled orders - Evaluation of discounts and promotions to measure effectiveness - Customer-level analysis to identify key drivers of revenue - Clear visualizations and structured reporting for decision-making Note: This video does not contain audio. All insights are presented through code walkthroughs, tables, and charts for a focused, distraction-free learning experience. Subscribe to see more portfolio projects where Spark SQL and R bring data to life with powerful analytics.