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, Mage Data Pipeline Tool, BigQuery, and Looker Studio.

Host data on GCP using Google Storage (store s3 file).

Run Mage on local machine fetching data from GCP and then writing on bigQuery.

Technology Used

• Programming Language - Python

Google Cloud Platform

1. Google Storage
2. BigQuery
3. 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.

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

Credits

https://github.com/darshilparmar/uber-etl-pipeline-data-engineering-project/tree/main