Uber with SQL

SQL
Python
Business Analysis
Exploratory analysis of a ride-sharing database using SQL and Python to investigate demand patterns, revenue performance, customer behavior, and operational efficiency.
Published

June 20, 2026

Uber car Erik Mclean, pexels.com

uber_with_sql

Ride-sharing companies generate large volumes of operational data covering trips, drivers, riders, pricing, and cancellations. The objective of this project is to use SQL to explore an Uber-like transactional database and identify operational and financial insights that could help management improve service quality, driver performance, and revenue generation.

The analysis focuses on trip activity, demand patterns, revenue performance, driver contribution, and cancellation behavior. All analyses were performed using SQL queries against a relational database, with key results visualized in Power BI.

Data for this project are from from Uber SQL database

Goals:

  1. Researching data
  2. Checking for duplicity
  3. Finding key indicators:
    • KPI for Trips
    • Revenue by city
    • Top pick-up zones
    • Demand hours
    • Cancellation reasons
    • Driver leaderboard by revenue
    • Drivers revenue as rider value

Tools:

Analysis

Open the notebook
Interactive dashboard
Original Github Repository