This project demonstrates the development of a scalable, cloud-based analytics pipeline for processing and visualizing taxi trip data using Google Cloud Platform. The end-to-end system extracts meaningful business insights to support data-driven decision-making in transportation operations.
🔧 Key Features and Components:
📁 Data Ingestion: Raw taxi trip data is stored in GCP Cloud Storage for scalable and secure data management.
🧮 Data Processing: Utilized Python scripts on GCP Compute Engine to clean, transform, and prepare the data for analysis. Implemented logic for calculating trip statistics, patterns, and performance metrics.
🔄 ETL Pipeline Automation: Integrated the Mage data pipeline tool to automate data workflows and streamline the ETL process across services.
📊 Data Warehousing and Querying: Loaded the processed data into BigQuery for high-performance SQL querying and analytical operations.
📈 Data Visualization: Built interactive dashboards in Looker Studio (formerly Data Studio) to present key insights such as: Ride volume trends Revenue analysis Trip duration and distance breakdowns Geographic heatmaps of pickups and drop-offs
🎯 Business Insights: Extracted actionable patterns and trends to optimize taxi operations and support strategic planning. Enabled stakeholders to make informed decisions based on real-time, cloud-driven analytics.
☁️ Cloud-Native Architecture: Designed a modular and scalable architecture entirely on GCP, demonstrating hands-on proficiency with cloud analytics tools.