Uber Data Analytics | Modern Data Engineering GCP Project

Introduction

The goal of this project is to perform data analytics on Uber data using various tools and technologies, including GCP Storage, Python, Compute Instance, Mage Data Pipeline Tool, BigQuery, and Looker Studio.

Architecture

Technology Used

• Programming Language - Python

Google Cloud Platform

1. Google Storage
2. Compute Instance
3. BigQuery
4. Looker Studio

Modern Data Pipeine Tool - https://www.mage.ai/

Contibute to this open source project - https://github.com/mage-ai/mage-ai

Dataset Used

TLC Trip Record Data Yellow and green taxi trip records include 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.

Here is the dataset used in the video - [https://github.com/Aakaaaassh/Uber_GCP_BigQuery_MageAI_DE_Project/blob/main/Uber%20Data/uber_data.csv]

More info about dataset can be found here:

1. Website - https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page
2. Data Dictionary - https://www.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf

Data Model

License

This project is licensed under the MIT License - see the LICENSE file for details.