End-to-End ETL and SQL Analysis of OLA Ride Data
In this SQL-based project, we analyze ride and booking data for OLA, one of India’s largest ride-hailing services. The goal is to extract meaningful insights from the ride, booking, payment, customer, and driver datasets to support business decisions and enhance service quality. The project involves querying data to understand booking behavior, cancellation reasons, payment preferences, customer engagement, and ride quality metrics.
Through this project, we demonstrate the ability to write optimized SQL queries to perform aggregations, filters, joins, and groupings. These queries help answer real-world business questions such as identifying top customers, cancellation trends, preferred payment methods, and rating distribution
1. Retrieve All Successful Bookings
This query filters the dataset to return only the bookings where the ride was successfully completed, helping analyze completed transactions.
2. Average Ride Distance by Vehicle Type
This query calculates the average ride distance for each vehicle type, providing insights into vehicle usage patterns and trip lengths.
3. Total Cancelled Rides by Customers
This query counts how many times customers canceled their rides, offering valuable data on user behavior and service dissatisfaction.
4. Top 5 Customers by Number of Successful Bookings
This query identifies the top 5 most frequent users based on completed bookings, useful for customer loyalty and retention analysis.
5. Rides Cancelled by Drivers (Personal & Car Issues)
This query counts the total number of rides that drivers canceled due to personal or car-related problems, helping identify common operational issues.
6. Max and Min Driver Ratings for Prime Sedan Bookings
This query fetches the highest and lowest driver ratings specifically for Prime Sedan rides, helping assess service quality for that vehicle segment.
7. Rides Paid via UPI
This query returns all bookings where customers used UPI as the payment method, giving insights into digital payment adoption among users.
8. Average Customer Rating per Vehicle Type
This query calculates the average rating given by customers for each vehicle type, highlighting satisfaction trends across different ride categories.
9. Total Booking Value of Successful Rides
This query sums up the value of all rides that were successfully completed, offering a snapshot of the platform’s revenue from fulfilled trips.
