Data Scientists, Analysts, Engineers at Southwest Airlines use SQL in their role on the daily. They use SQL for analyzing flight data to optimize operations and generating customer behavior insights for marketing strategies. That's why Southwest Airlines LOVES to ask folks interviewing at the company SQL coding interview questions.
To help you study for the Southwest Airlines SQL interview, we've curated 8 Southwest Airlines SQL interview questions – scroll down to start solving them!
Southwest Airlines wants to identify their VIP customers. In order to identify the power users, we are tasked to find the customers who have flown more than 100 times per year. Write a SQL query to find out these customers' ids and the total number of flights they took in the past year.
Given two tables, and , which have the following structure and sample data:
customer_id | first_name | last_name |
---|---|---|
101 | John | Doe |
102 | Jane | Johnson |
103 | Sam | Smith |
104 | Emma | Davis |
flight_id | customer_id | flight_date |
---|---|---|
2001 | 101 | 06/08/2021 00:00:00 |
2002 | 101 | 06/10/2021 00:00:00 |
2003 | 102 | 06/18/2021 00:00:00 |
2004 | 103 | 06/18/2021 00:00:00 |
2005 | 102 | 07/26/2021 00:00:00 |
2006 | 102 | 07/05/2021 00:00:00 |
2007 | 101 | 08/05/2021 00:00:00 |
Note: The flight dates are given in the format 'MM/DD/YYYY'.
The PostgreSQL query to solve this would be:
This query joins the customers and the flights tables on the customer_id field, then filters for flights that took place within the past year (using the BETWEEN clause). It then groups the results by customer_id and counts the number of flights for each customer (using the COUNT() function). The HAVING clause is used to filter groups (after the GROUP BY clause) that have more than 100 flights.
To practice a related customer analytics question on DataLemur's free online SQL code editor, try this recently asked Microsoft SQL interview question:
Suppose you had a table of Southwest Airlines employee salary data, along with which department they were in. Write a query to compare the average salary of employees in each department to the company's average salary for March 2024. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
Check out the SouthWest Airlines One Report for more context!
Try this problem and run your code right in DataLemur's online SQL environment:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution with hints here: Department Salaries.
The clause is used to remove all duplicate records from a query.
For example, if you had a table of open jobs Southwest Airlines was hiring for, and wanted to see what are all the unique job titles that were currently available at the company, you could write the following query:
Southwest Airlines would like to understand how delays are distributed across their different departure cities. You have been given a dataset that contains information about each flight, including its departure city, arrival city, scheduled departure time, actual departure time, and delay time (in minutes). {#Question-4}
Please write a SQL query to identify the Top 5 departure cities with the highest average flight delay time in year 2021.
Below is the provided table:
flight_id | departure_city | arrival_city | scheduled_departure_time | actual_departure_time | delay_time |
---|---|---|---|---|---|
315 | Los Angeles | Denver | 2021-01-02 07:30:00 | 2021-01-02 08:00:00 | 30 |
701 | New York | Los Angeles | 2021-01-10 12:00:00 | 2021-01-10 12:45:00 | 45 |
922 | Chicago | New York | 2021-01-18 16:00:00 | 2021-01-18 16:00:00 | 0 |
503 | Los Angeles | Chicago | 2021-02-25 20:00:00 | 2021-02-25 20:45:00 | 45 |
791 | New York | Seattle | 2021-05-23 11:00:00 | 2021-05-23 13:00:00 | 120 |
454 | Chicago | Dallas | 2021-06-30 15:30:00 | 2021-06-30 15:45:00 | 15 |
Here is the PostgreSQL query to get the top 5 departure cities with the highest average flight delay in 2021:
The query groups the data by departure city and calculates the average delay time for each city. It then orders these averages in descending order and limits the output to only the top 5 cities. Please note that this query will only include flights that were scheduled to depart in 2021.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
Some similarities between unique and non-unique indexes include:
Some differences between unique and non-unique indexes include:
Southwest Airlines would like to implement a new marketing strategy. For that, they are interested in analyzing their customer flight records based on specific conditions. They want to know the count of customers who have frequently traveled between Dallas and Houston in last quarter, and have also availed in-flight meals. A frequent traveler is defined as a customer who has made more than 4 trips in the last quarter.
flight_id | customer_id | source | destination | flight_date | inflight_meal_service |
---|---|---|---|---|---|
101 | C001 | Dallas | Houston | 01/04/2022 | Yes |
102 | C002 | Dallas | Austin | 01/05/2022 | Yes |
103 | C001 | Houston | Dallas | 01/10/2022 | Yes |
104 | C002 | Austin | Dallas | 02/14/2022 | Yes |
105 | C001 | Dallas | Houston | 03/10/2022 | Yes |
106 | C003 | New York | Boston | 03/15/2022 | No |
107 | C001 | Houston | Dallas | 03/20/2022 | Yes |
108 | C003 | Boston | New York | 03/25/2022 | No |
109 | C001 | Dallas | Houston | 03/30/2022 | Yes |
This query counts the number of customers who meet all the specified conditions:
It uses the , , and SQL clauses to filter and aggregate the data. It also makes use of clause to filter data on single or multiple conditions. The command is used to filter the data within a specific date range.
A UNIQUE constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.
For example, if you had Southwest Airlines employee data stored in a database, here's some constraints you'd use:
In the Southwest Airlines employee example, the UNIQUE constraint is applied to the "email" field to ensure that each employee has a unique email address. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two employees had the same email address.
Find the average delay time per flight route for Southwest Airlines. Delay is defined as the difference between scheduled departure time and actual departure time. For the purpose of this problem, consider positive delays only (i.e., the flights that left later than scheduled).
We'll have a table, and each row corresponds to a single one-way flight.
flight_id | route_id | scheduled_departure_time | actual_departure_time |
---|---|---|---|
1 | 100 | 08/01/2022 10:00:00 | 08/01/2022 10:35:00 |
2 | 200 | 08/02/2022 15:30:00 | 08/02/2022 15:50:00 |
3 | 100 | 08/02/2022 10:00:00 | 08/02/2022 10:30:00 |
4 | 300 | 08/03/2022 12:00:00 | 08/03/2022 12:00:00 |
5 | 200 | 08/03/2022 15:30:00 | 08/03/2022 15:55:00 |
route_id | avg_delay(mins) |
---|---|
100 | 17.5 |
200 | 12.5 |
Delay times were calculated as follows: For route 100, (35 + 30)/2 = 17.5 minutes. For route 200, (20 + 25)/2 = 12.5 minutes. Route 300 was not delayed, so it didn't show in the results.
The query should look something like this:
This SQL query first subtracts from to calculate delay for each flight. It only considers cases where is greater than (meaning these are delayed flights). The function then converts the time interval to seconds (as ), and we divide by 60 to get minutes. The function is applied to these delay times grouped by , to find the average delay per route. If there is no delay for any flight for a certain route, that route will not shop up in the output.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating average metrics or this Twitter Tweets' Rolling Averages Question which is similar for time-series averages.
The key to acing a Southwest Airlines SQL interview is to practice, practice, and then practice some more! In addition to solving the earlier Southwest Airlines SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each exercise has hints to guide you, step-by-step solutions and crucially, there is an online SQL code editor so you can right online code up your SQL query and have it checked.
To prep for the Southwest Airlines SQL interview you can also be wise to solve interview questions from other airlines like:
In case your SQL foundations are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.
This tutorial covers SQL concepts such as filtering data with boolean operators and filtering data with WHERE – both of these show up routinely during SQL job interviews at Southwest Airlines.
Besides SQL interview questions, the other topics to prepare for the Southwest Airlines Data Science Interview are:
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