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
- Google Storage
- Compute Instance
- BigQuery
- 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/darshilparmar/uber-etl-pipeline-data-engineering-project/blob/main/data/uber_data.csv
More info about dataset can be found here:
- Website - https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page
- Data Dictionary - https://www.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf
Data Model
Complete Video Tutorial
Video Link - https://youtu.be/WpQECq5Hx9g