At Garmin, SQL is used all the damn time for analyzing customer device usage patterns, and optimizing their navigational software functionality based on GPS-data insights. They even support SQL to query Garmin aviation databases. That's why Garmin frequently asks SQL problems in interviews for Data Science, Data Engineering and Data Analytics jobs.
To help you study for the Garmin SQL interview, we've curated 11 Garmin SQL interview questions – how many can you solve?
Garmin is a company that is known for its specialization in GPS technology for automotive, aviation, marine, outdoor, and sport activities. As an important part of Garmin's business, the company would like to find out their most valuable customers. These customers, often referred to as 'whale' customers, generate a significant part of Garmin's revenue.
More specifically, Garmin is interested in discovering which customers have made the most purchases in a specific time frame (e.g., last six months). This information is important from a customer relationship point of view, as retaining these customers may be very beneficial to the business.
You have got access to the customer and transactions database. The task is to write a SQL query to identify the top 10 'whale' customers based on the total amount of money spent in the last six months.
Here is a basic schema for two tables, and :
customer_id | first_name | last_name | |
---|---|---|---|
1 | John | Doe | john.doe@garmin.com |
2 | Jane | Smith | jane.smith@garmin.com |
3 | Sam | Houston | sam.houston@garmin.com |
transaction_id | customer_id | transaction_date | amount |
---|---|---|---|
20001 | 1 | 2021-09-25 | 250.00 |
20002 | 2 | 2021-10-13 | 500.00 |
20003 | 1 | 2022-01-15 | 350.00 |
With the above data, the PostgreSQL query could be the following:
This query adds up the total amount of all transactions each customer made within the last six months. The results are then ordered in descending order of the total amount spent, and only the top 10 customers are returned. This way, you can identify the top 'whale' customers of Garmin.
To practice a similar VIP customer analysis question on DataLemur's free online SQL code editor, try this Microsoft SQL Interview problem:
Using a dataset of product reviews, we're interested in finding the average review score (stars) that each product received in each month. We want to analyze performance of products and see if there's any significant shift in customers' sentiment.
The reviews dataset is a collection of rows where each row corresponds to an individual review with fields for the review_id (a unique identifier for the review), user_id (the identifier for the user who submitted the review), submit_date (date when the review was submitted), product_id (the identifier for the product being reviewed), and stars (the rating score given by the user).
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2022-06-08 | 50001 | 4 |
7802 | 265 | 2022-06-10 | 69852 | 4 |
5293 | 362 | 2022-06-18 | 50001 | 3 |
6352 | 192 | 2022-07-26 | 69852 | 3 |
4517 | 981 | 2022-07-05 | 69852 | 2 |
We want output with columns for the month (mth), product_id, and average stars (avg_stars) that product received in that month.
mth | product_id | avg_stars |
---|---|---|
6 | 50001 | 3.50 |
6 | 69852 | 4.00 |
7 | 69852 | 2.50 |
This PostgreSQL query extracts the month from the submit_date and groups the data by this and the product_id. This means that for each product, we will calculate an independent average for each month. Afterwards, ordering is done based on the product_id and mth.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
In database schema design, a one-to-one relationship between two entities means that each entity is linked to a single instance of the other. For example, the relationship between a car and a license plate is one-to-one because each car has only one license plate, and each license plate belongs to one car.
In contrast, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. An example of this is the relationship between a teacher and their classes - a teacher can teach multiple classes, but each class is only associated with one teacher.
Garmin, being a multinational technology company, is interested in understanding the sales dynamics and customer feedback for their various products, specially their flagship devices like GPS units, Smart Watches, and Action Cameras from different regions. Assume you are given two tables: one for Device Sales (sales) and one for Customer Reviews (reviews). The tables have the following structure:
sales_id | product_id | region | sales_date | units_sold |
---|---|---|---|---|
9012 | GPS3001 | North America | 01/06/2022 | 500 |
7643 | WATCH44 | Europe | 01/08/2022 | 400 |
8326 | CAMPRO2 | Asia | 02/18/2022 | 350 |
9073 | GPS3001 | Europe | 02/22/2022 | 550 |
2918 | WATCH44 | South America | 03/02/2022 | 600 |
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 01/15/2022 | GPS3001 | 4 |
7802 | 265 | 01/25/2022 | WATCH44 | 5 |
5293 | 362 | 02/20/2022 | GPS3001 | 3 |
6352 | 192 | 02/28/2022 | WATCH44 | 4 |
4517 | 981 | 03/05/2022 | CAMPRO2 | 2 |
Write a SQL query that calculates the average review stars and total units sold for each product broken down by region for the Q1 of 2022.
In this query, we join the reviews data with the sales data on their matching . We only consider reviews in the Q1 of 2022 as mentioned in the question. We then group by product and region to get the average stars and total units sold per product on regional basis.
To identify records in one table that do not appear in another, you can use a LEFT JOIN and examine NULL values in the right-side table.
Say for example you had exported Garmin's CRM (Customer Relationship Management) database into PostgreSQL, and had a table of sales leads, and a second table of companies.
Here's an example of how a query could find all sales leads that are not associated with a company:
This query brings back all rows from the sales leads table, along with any matching rows from the companies table. If there is no matching row in the companies table, NULL values will be returned for all of the right table's columns.
We then filter out out any rows where the column is , leaving only the sales leads that are NOT associated with a company.
Garmin a popular company well known for producing fitness & outdoor devices. On Garmin devices, users can keep track of the number of steps they've taken in a day. This helps users to gauge their fitness activity. As a data analyst interviewing at Garmin, your task is to find the average number of steps a Garmin user records per day. Garmin keeps track of this data daily for every user.
Here is a sample data table to illustrate the problem:
user_id | date | steps |
---|---|---|
1 | 2022-01-01 | 8000 |
1 | 2022-01-02 | 7500 |
2 | 2022-01-01 | 7000 |
2 | 2022-01-02 | 6500 |
2 | 2022-01-03 | 8500 |
3 | 2022-01-01 | 9000 |
In the example above, for example, the user with =1 took 8,000 steps on January 1st, 2022, and 7,500 steps on January 2nd, 2022.
From the table, generate a query that shows the average number of steps each Garmin user took per day.
user_id | average_steps |
---|---|
1 | 7750 |
2 | 7333.33 |
3 | 9000 |
In the query above, we grouped our data by , then used the function to calculate the average number of steps per . We used to format the output with 2 decimals.
To practice a very similar question try this interactive Google Median Google Search Frequency Question which is similar for average calculation or this Twitter Tweets' Rolling Averages Question which is similar for daily activity tracking.
A is a column or set of columns in a table that references the primary key of another table. It is used to link the two tables together, and to ensure that the data in the foreign key column is valid.
The constraint helps to ensure the integrity of the data in the database by preventing the insertion of rows in the foreign key table that do not have corresponding entries in the primary key table. It also helps to enforce the relationship between the two tables, and can be used to ensure that data is not deleted from the primary key table if there are still references to it in the foreign key table.
For example, if you have a table of Garmin customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the Garmin customers table.
Garmin is an multinational company that specializes in GPS technology development for its use in automotive, aviation, marine, outdoor, and sport activities and utilities. In this context, you have access to the customer records database, specifically to the table . This table includes the columns , , , , , and . Your task is to write an SQL query to find all customers who live in the United States () and whose email addresses end with "gmail.com".
customer_id | first_name | surname | city | country | |
---|---|---|---|---|---|
1 | John | Doe | johndoe@gmail.com | Seattle | USA |
2 | Jane | Smith | janesmith@yahoo.com | New York | USA |
3 | Alice | Johnson | alicejohnson@gmail.com | Los Angeles | USA |
4 | Bob | Williams | bobwilliams@hotmail.com | Chicago | USA |
5 | Charlie | Brown | charliebrown@gmail.com | Houston | USA |
This PostgreSQL query will return a table with all customers who live in the USA and whose email addresses end with "gmail.com". It uses the LIKE keyword to match a pattern within the column and checks if the column is 'USA' for each record.
Garmin needs a SQL query for their customer database report. The objective is to find the average price of purchased products per customer. Two tables will be used to gather essential data - a Customers table and a Products table. Below are their structures and some sample data:
customer_id | first_name | last_name | |
---|---|---|---|
101 | John | Doe | john.doe@email.com |
102 | Jane | Smith | jane.smith@email.com |
103 | Robert | Jones | robert.jones@email.com |
purchase_id | customer_id | product_id | purchase_date |
---|---|---|---|
501 | 101 | 001 | 2021-10-25 |
502 | 101 | 002 | 2021-11-2 |
503 | 102 | 001 | 2021-11-10 |
504 | 103 | 003 | 2021-09-15 |
505 | 102 | 002 | 2021-08-20 |
product_id | product_name | price |
---|---|---|
001 | Garmin Forerunner 35 | 100.00 |
002 | Garmin GPSMAP 64st | 250.00 |
003 | Garmin Drive 52 | 120.00 |
This SQL query joins the three tables - Customers, Purchases, and Products - using INNER JOIN clause. Customer data and Purchase data are joined on the customer_id, and Purchase data and Product data on product_id. The query produces the output that lists each customer's first and last name, email, and the average price of the products they've purchased. Grouping the results by the customer_id ensures each resulting row corresponds to a distinct customer, and the AVG function calculates the average purchase price.
Because joins come up so often during SQL interviews, try this interactive Snapchat SQL Interview question using JOINS:
To explain the difference between a primary key and foreign key, let's start with an example Garmin sales database:
:
+------------+------------+------------+------------+ | order_id | product_id | customer_id| quantity | +------------+------------+------------+------------+ | 1 | 303 | 1 | 2 | | 2 | 404 | 1 | 1 | | 3 | 505 | 2 | 3 | | 4 | 303 | 3 | 1 | +------------+------------+------------+------------+
In this table, could be a primary key. It is unique for each row in the table and cannot contain null values.
and could both be foreign keys. They reference the primary keys of other tables, such as a Products table and a Customers table, respectively. This establishes a relationship between the table and the other tables, such that each row in the sales database corresponds to a specific product and a specific customer.
Garmin provides GPS tracking devices. The position of the devices, along with their timestamps, are logged into a database. Let's consider the cases where devices are used for measuring the distance traveled by a user in a single session and the average speed during that session.
The table registers each GPS reading, with the latitude and longitude of the point, the device_id and the timestamp of the reading.
log_id | device_id | timestamp | latitude | longitude |
---|---|---|---|---|
10001 | 907 | 2022-09-01 08:00:00 | -34.6037 | -58.3816 |
10002 | 907 | 2022-09-01 08:01:00 | -34.6031 | -58.3820 |
10003 | 907 | 2022-09-01 08:02:00 | -34.6043 | -58.3824 |
10004 | 908 | 2022-09-01 08:00:00 | -32.5228 | -55.7658 |
10005 | 908 | 2022-09-01 08:01:00 | -32.5222 | -55.7650 |
The task is to create a function which returns the , the , total distance travelled (in meters, rounded to the nearest meter), and the average speed (in meters/second, rounded to 2 decimal places) for a given device on a specified date.
The Earth's radius is approximately 6371km. You can use the haversine formula to calculate the distance between two geographic coordinates.
The function uses a loop to iterate over the GPS readings for the specified device and date, calculating the distance between the current and previous readings using the haversine formula. The total distance travelled and time spent are accumulated, and the average speed is then calculated as distance divided by time. The results are rounded as required.
To practice a very similar question try this interactive Google Odd and Even Measurements Question which is similar for se of SQL to analyze sensor measurements or this Twitter Tweets' Rolling Averages Question which is similar for use of SQL to analyze data over a time series.
The best way to prepare for a Garmin SQL interview is to practice, practice, practice. Beyond just solving the earlier Garmin SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.
Each SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there's an online SQL code editor so you can right in the browser run your query and have it checked.
To prep for the Garmin SQL interview you can also be wise to practice interview questions from other tech companies like:
But if your SQL coding skills are weak, forget about going right into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as aggregate window functions and Subqueries – both of these pop up routinely during SQL job interviews at Garmin.
Besides SQL interview questions, the other question categories covered in the Garmin Data Science Interview are:
To prepare for Garmin Data Science interviews read the book Ace the Data Science Interview because it's got: