This project focuses on building an end-to-end data engineering pipeline on Google Cloud Platform (GCP) to process, analyze, and visualize NYC Uber trip data. It involves ingesting raw trip data, transforming it for efficient querying, and leveraging BigQuery and Looker Studio to generate insights. The goal is to demonstrate how cloud-based data solutions can enable real-time analytics and data-driven decision-making for transportation businesses.

Languages:

Python

SQL

Google Cloud Platform:

Google Storage

Google Engine

Big Query

Looker Studio

Modern Data Pipeline Tool:

Mage - https://www.mage.ai

✅ Scalable & Efficient Data Processing – GCP’s cloud infrastructure ensures fast, scalable, and cost-effective data handling. ✅ Real-Time Insights – Using BigQuery and Looker Studio, the project enables quick analysis of Uber trip trends, peak hours, and demand patterns. ✅ Automated ETL Workflow – Reduces manual effort in data ingestion and transformation, ensuring clean and structured data for analytics. ✅ Business Impact – Provides actionable insights for ride-sharing platforms, urban planners, and businesses relying on mobility data.