An end-to-end modern data engineering project, including deployment of an ETL pipeline on Google Cloud Platform, using BigQuery for data analysis and leveraging Looker to generate an insight dashboard.
Business Value Proposition
This comprehensive end-to-end Uber data engineering solution is designed to transform the way we understand and analyze ride-sharing data. Through the implementation of this data solution, we aim to:
- Enhance Trip Insights: Provide actionable insights into ride patterns and customer behavior.
- Identify Service Improvement Opportunities: Uncover areas where service quality can be enhanced.
- Optimize Driver Performance: Identify top-performing and underperforming drivers to improve overall service delivery.
Technology Stack
Languages:
- Python
- SQL
Google Cloud Platform:
- Google Storage
- Google Engine
- BigQuery
- Looker Studio
Modern Data Pipeline Tool:
- Mage - mage.ai
Data Source
The dataset is provided by TLC Trip Record Data, including yellow and green taxi trip records. It includes fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts.
Data source link: TLC Trip Record Data
Data Dictionary: Data Dictionary
Data Modeling
ETL Pipeline
Looker Dashboard
Conclusion
This end-to-end data engineering project demonstrates the power of modern cloud technologies in processing and analyzing large-scale ride-sharing data. By leveraging Google Cloud Platform, BigQuery, and Looker Studio, we’ve created a robust pipeline that transforms raw Uber trip data into actionable insights. This solution provides a foundation for data-driven decision-making in the ride-sharing industry, offering valuable insights into trip patterns, driver performance, and service optimization opportunities.