SQL-based analysis of Apple retail sales data to uncover insights on sales trends, product performance, and warranty claims using large-scale datasets.

This project showcases advanced SQL querying techniques that I used to perform an analysis of over 1 million rows of Apple retail sales data. The dataset includes information about products, stores, sales transactions, and warranty claims across various Apple retail locations globally.

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Apple Retail Sales Data Analysis using SQL

SQL-based analysis of a 1M+ row Apple retail sales dataset covering sales trends, warranty claims, store performance, and advanced analytics using PostgreSQL.

Apple Retail Sales Analysis SQL Project – A data-driven project using advanced SQL techniques to analyze over half million rows of Apple retail sales and warranty data. Extracts insights on sales performance, product quality, customer behavior, and profitability to support strategic business decisions.

A SQL-based data analysis project using 1M+ rows of simulated Apple retail sales data. Includes optimized queries, trend analysis, product performance insights, revenue patterns, and advanced SQL techniques like joins, window functions, and subqueries.

The Apple Retail Sales Analysis project uses SQL to analyze over 1 million sales records from 70+ stores across 15+ countries. It explores product performance, sales trends, and warranty claims, applying advanced SQL techniques like joins, aggregations, and window functions to derive actionable business insights.

Apple Store Analytics SQL Project – Designed a relational retail database system and performed advanced SQL analysis using joins, subqueries, aggregations, CTEs, and window functions to generate business insights on sales performance, pricing trends, and warranty claims.

Analyzing over 1 million Apple retail sales records using SQL to extract insights, optimize queries, and improve data-driven decision-making. Focuses on advanced querying, performance tuning, and real-world business analysis.

This project analyzes over 1 million rows of Apple retail sales data using advanced SQL techniques. It covers key insights on sales trends, product performance, store analysis, and warranty claims across various Apple locations. The project demonstrates SQL proficiency in joins, window functions, aggregations, and query optimization for large-scale

Designed and implemented a normalized SQL database for Apple Retail Sales analysis. The project includes ER diagram design, schema creation, and 10+ analytical queries to derive insights on sales performance, store revenue, product trends, and warranty claims using PostgreSQL

Executed an advanced Apple Retail Sales Data Analysis project using SQL, working with over 1 million rows across global store transactions. Applied complex joins, window functions, and correlation analysis to uncover insights on product performance, warranty trends, and sales growth across time and regions.

This project analyzes over 1 million rows of Apple retail sales data using SQL to uncover business insights. It includes schema design, indexing, and advanced queries to solve real-world sales and warranty-related problems. The work highlights expertise in data analysis, performance tuning, and decision-support through SQL.

This project involves deep analysis of Apple retail store sales using SQL. It includes creating a structured database, importing real-world-style CSV data, and running a series of complex queries to derive key business insights.