This project provides data-driven insights into Walmart's sales, profitability, and customer behavior using SQL analysis. The findings help optimize marketing, inventory, and operational strategies for increased revenue and efficiency.

Exploratory and financial analysis of Walmart retail sales with SQL, Python, and Business Intelligence.

Built an ETL pipeline for Walmart sales data, using Python (data cleaning) and SQL (exploration & KPI extraction) to deliver business intelligence insights on revenue trends, branch performance, and customer behavior.

📊 SQL-driven analysis of Walmart sales data exploring transactional trends, customer segmentation, and product performance. Demonstrates advanced SQL techniques (CTEs, window functions, dynamic aggregations) for business intelligence insights. Includes EDA, time-based analytics, and operational efficiency metrics.

saleDATAWalmart is a compact yet robust database for swift analysis of Walmart sales. Enriched with features like time_of_day, it offers quick insights. The README's concise SQL queries help understand unique cities, popular products, and revenue trends, making it an efficient tool for business intelligence from Walmart's sales data.

This project analyzes Walmart sales data using SQL and Python, focusing on key performance metrics such as revenue trends, branch performance, customer behavior, and product category insights. The goal is to generate actionable business intelligence to support strategic decision-making in retail operations.

Este proyecto utiliza un dataset de Walmart para construir un modelo de Business Intelligence usando DBT, SQL Server y Python. Se implementan tres capas de transformación: RAW, STAGING y MASTER, con el objetivo de identificar patrones de comportamiento de clientes y predecir el riesgo de churn.