This project analyzes 19M+ flight records (sourced from Kaggle in Parquet format) to uncover trends in delays, taxi times, and cancellations—driving insights for improved airline scheduling and operational efficiency.
View the Flight Data Visualization
- ETL Processing: Apache Spark (PySpark)
- Data Warehousing: Google BigQuery with a star schema for scalable OLAP analytics
- Visualization: Tableau for interactive dashboards
- Development: Python & Jupyter Notebook
- Data Extraction: Extracted flight records from Parquet files.
- Data Processing: Processed and transformed raw data using Spark and Python.
- Automation: Automated incremental updates to the data warehouse to integrate new flight records, airlines, and airports.
- Schema Design: Implemented a star schema in Google BigQuery.
- OLAP Queries: Developed comprehensive OLAP queries to facilitate multi-dimensional analysis, leading to faster and more scalable analytics.
- Dashboard Development: Built interactive dashboards using Tableau that integrate directly with BigQuery.
- Strategic Reporting: Provided stakeholders (executives, business analysts, operations teams, and marketing teams) with high-level and detailed reports to drive strategic decision-making.
This project is licensed under the MIT License.