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.
- Programming Language - Python
Google Cloud Platform
- Google Storage
- Compute Instance
- BigQuery
- Looker Studio
GCP Login detail - https://console.cloud.google.com/welcome?project=serious-azimuth-358905
Modern Data Pipeine Tool - https://www.mage.ai/
Looker Studio for dashboard creation - https://lookerstudio.google.com/u/0/navigation/reporting
Website created in mage for applying etl pipeline - https://35.244.17.11/6789/pipeline
ER model designing - https://lucid.app/
Uber ER deisned model - https://lucid.app/publicSegments/view/167b1b16-b9e6-4f1e-ac33-156a052cc5fa/image.png
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:
- 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
NOTE: REFER THE WORD FILE IN REPOSITORY TO KNOW THE STEPS FOR COMPLETING THE PROJECT