Flight-Punctuality-Data-warehouse-using-SQL-OLAP-and-Tableau

Overview

The Flight Punctuality Data Warehouse project aims to develop a robust system for storing, analyzing, and visualizing flight punctuality data. This system will help airlines, airports, and regulatory authorities to monitor and improve flight punctuality, understand delay patterns, and make data-driven decisions. The project will leverage SQL for data storage and management, OLAP for multi-dimensional analysis, and Tableau for dynamic data visualization.

Objectives

1. Data Collection and Integration:

   • Gather flight data from various sources, including airline schedules, airport logs, and weather data.
   • Integrate the collected data into a centralized SQL-based data warehouse.

2. Data Warehouse Design:

   • Design and implement a star schema in SQL to organize flight punctuality data, with fact tables for flight details and dimension tables for time, airlines, airports, and weather conditions.

3. OLAP Cube Creation:

   • Develop OLAP cubes to support multi-dimensional analysis of flight punctuality, enabling efficient querying and reporting on various metrics like average delay, on-time performance, and delay causes.

4. Data Analysis and Reporting:

   • Use OLAP tools to perform detailed analysis on flight data, uncovering trends and patterns related to punctuality.
   • Create dashboards and reports in Tableau to visualize key insights and metrics, making the data accessible to stakeholders.

Project Components

Data Collection and Integration

• Data Sources:
  • Airline schedules
  • Airport logs
  • Weather data
• Data Integration Tools:
  • ETL (Extract, Transform, Load) processes to clean, transform, and load data into the warehouse.

Data Warehouse Design

• Schema Design:
  • Fact Table:
    • FlightDetails: FlightID, AirlineID, AirportID, DateID, DepartureTime, ArrivalTime, DelayDuration, DelayReason.
  • Dimension Tables:
    • Airlines: AirlineID, AirlineName.
    • Airports: AirportID, AirportName, Location.
    • Date: DateID, Date, DayOfWeek, Month, Year.
    • Weather: WeatherID, AirportID, DateID, WeatherCondition.

OLAP Implementation

• OLAP Tools:
  • SQL Server Analysis Services (SSAS) or similar.
• Cube Design:
  • Cubes for analyzing delays by airline, airport, time period, and weather conditions.

Visualization with Tableau

• Dashboards and Reports:
  • Overall Flight Punctuality: Visualizing on-time performance metrics.
  • Delay Analysis: Breakdown of delays by cause, airline, airport, and time.
  • Trend Analysis: Identifying patterns and trends in flight delays over time.
• Interactive Features:
  • Filters for specific airlines, airports, and time frames.
  • Drill-down capabilities to explore detailed data.

Benefits

• Improved Decision Making: Provide stakeholders with actionable insights to enhance flight punctuality.
• Enhanced Operational Efficiency: Identify root causes of delays and implement strategies to mitigate them.
• Better Customer Satisfaction: Reduce delays and improve overall travel experience for passengers.

Conclusion

The Flight Punctuality Data Warehouse project will provide a comprehensive solution for monitoring and improving flight punctuality through efficient data storage, advanced analysis, and interactive visualizations. By leveraging SQL, OLAP, and Tableau, this project will deliver valuable insights and help stakeholders make informed decisions to enhance the overall efficiency of air travel.