I recently completed an exciting SQL project analyzing air cargo data to enhance business strategies and improve decision-making in the logistics sector. This hands-on project focused on database management and querying, uncovering critical insights into cargo trends, customer preferences, and operational efficiency.
Project Highlights and Objectives Database Design:
Created a relational database schema named AirCargoDB to organize cargo and travel data effectively. Designed tables including: Customer: Containing information about customers.
passengers_on_flights: Storing details of travel and flight-related information.
ticket_details: Managing ticket-related information.
routes: Capturing detailed data about the cargo routes.
Data Cleaning and Preparation: Processed and prepared raw data to ensure accuracy by addressing inconsistencies, handling missing values, and removing outliers. Querying and Analysis:
Extracted actionable insights using SQL, such as:
Identifying the busiest cargo routes and their revenue contribution. Analyzing customer preferences based on cargo and ticketing history. Understanding trends in shipment performance across different time periods.
Performance Optimization:
Applied indexing and query optimization techniques to improve database performance and enable faster data retrieval from large datasets. Technologies Used SQL: For querying and managing the database. SQLite: For database implementation and testing. This project demonstrated my ability to design and optimize databases while translating raw air cargo data into insights that can guide strategic decision-making. It highlighted my technical expertise in SQL and my potential to contribute to the success of the logistics and travel industry.
#SQL #DataAnalytics #AirCargoAnalysis