SQL Airline & Retail Data Analysis
📌 Overview This project demonstrates SQL-based data analysis using PostgreSQL on airline and retail datasets. The focus is on extracting insights using joins, aggregations, window functions, and business logic.
🎯 Key Objectives Analyze airline data (flights, bookings, tickets)
Perform retail analysis (stores, orders, products)
Apply real-world business logic using SQL
Showcase advanced SQL skills for data analysis roles
🛠️ Tools & Technologies PostgreSQL SQL
📊 Key Tasks Performed
Performed joins across multiple tables
Used GROUP BY and aggregations for analysis
Applied window functions (RANK) for ranking insights
Built Common Table Expressions (CTEs)
Derived business insights (revenue, customer spending)
Used CASE statements for categorization
📂 Project Structure airline_analysis.sql → Contains all SQL queries used in the project
🔍 Sample Analysis
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Tickets without boarding passes --> Identified tickets that do not have boarding passes using LEFT JOIN.
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Booking date formatting --> Converted booking dates into YYYY-MM-DD format using date functions.
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Most popular product per store --> Used CTE + RANK() to identify top-selling products.
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Airport ranking --> Ranked airports based on number of departures.
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Revenue analysis --> Calculated total revenue generated by each store.
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Customer analysis --> Identified top customers based on spending.
🚀 Key Learnings Writing optimized SQL queries
Using window functions for advanced analysis
Handling real-world datasets
Converting raw data into meaningful insights
📎 Author Sai Sandeep Kuchupudi , Business Intelligence Analyst
Skills: SQL | Power BI | Excel | Data Analysis