Retail Sales Analytics — Microsoft Fabric, SQL & Power BI

Retail Sales Analytics — Microsoft Fabric, SQL & Power BI This project is an end-to-end retail sales analytics solution developed to transform transactional sales data into actionable business insights for management and decision-making. The analysis uses the Superstore dataset containing 9,994 transactions across 21 columns from 2014–2017. The project began with data preparation in Microsoft Fabric. The source Excel dataset was loaded into a Fabric Notebook using Pandas, where column names were standardized before the data was converted into a Spark DataFrame. The transformed dataset was then written as a Delta table in the Fabric Lakehouse, making it available for SQL-based analysis. Using the SQL Analytics Endpoint, I developed 32 SQL queries covering data exploration, filtering, aggregations, customer and product analysis, geographic performance, and time-series analysis. The project also demonstrates more advanced analytical SQL techniques, including subqueries, Common Table Expressions (CTEs), ROW_NUMBER(), RANK(), DENSE_RANK(), running totals, percentage-of-total calculations, and profit-margin analysis. These queries were used to investigate sales performance, profitability, customer segments, products, regions, states, and trends over time. The reporting layer was developed in Power BI Desktop, where I created an interactive Retail Sales Performance Dashboard and a separate Executive Report designed for management review. The dashboard includes KPIs such as Total Sales, Total Profit, Total Orders, Total Quantity, Average Order Value, and Profit Margin, with year-over-year and month-over-month comparisons. Interactive filters allow users to analyze performance by region, category, customer segment, state, and date. Key findings included strong performance from the Technology category, the West region having the highest regional profit, Consumer customers representing the largest share of sales, and stronger sales activity toward the end of the year. The analysis also identified several negative-profit states, highlighting an important distinction between generating revenue and generating profitable revenue. Overall, this project demonstrates my ability to work across data preparation, Microsoft Fabric, SQL analysis, Power BI reporting, and business insight generation, translating raw transactional data into a solution that supports data-driven decision-making. GitHub: https://github.com/Rerri33/Retail-Sal...