Uber's Operations team wants a quick quality check on trip ETA data from the Driver app. Write a SQL query to calculate the overall average ETA and identify trips whose ETA is unusually high.
eta_minutes is not NULL.eta_minutes is greater than 2 times the overall average ETAavg_eta_minutes and outlier_trip_count.Because this is an easy question, use a single table only.
trip_eta_events
| column | type | description |
|---|---|---|
| trip_id | INT | Unique trip identifier |
| city_name | VARCHAR(50) | City where the trip was requested |
| rider_surface | VARCHAR(50) | Uber surface where ETA was shown |
| eta_minutes | DECIMAL(5,2) | Estimated time of arrival in minutes |
| event_date | DATE | Date of the ETA event |
| trip_id | city_name | rider_surface | eta_minutes | event_date |
|---|---|---|---|---|
| 1008 | Chicago | UberX | 18.00 | 2024-06-04 |
| 1002 | New York | UberX | 7.00 | 2024-06-01 |
| 1005 | San Francisco | Uber Reserve | 25.00 | 2024-06-03 |
| 1001 | New York | UberX | 5.00 | 2024-06-01 |
| 1009 | Chicago | UberXL | 0.00 | 2024-06-04 |
| 1004 | San Francisco | UberX | 6.00 | 2024-06-02 |
| 1007 | Chicago | UberX | NULL | 2024-06-03 |
| 1003 | New York | Uber Black | 9.00 | 2024-06-02 |
| 1006 | San Francisco | UberX | 40.00 | 2024-06-03 |
| 1010 | Miami | UberX | 8.00 | 2024-06-05 |
| avg_eta_minutes | outlier_trip_count |
|---|---|
| 13.11 | 2 |