Data Scientists, Analysts, and Data Engineers at Brinker International use SQL queries to analyze sales trends across their restaurant franchises, helping them identify peak times and popular menu items. They also use SQL to optimize inventory management by examining customer purchasing patterns, ensuring that each location has the right stock to meet demand, this is why Brinker International includes SQL questions during interviews.
Thus, to help you prep, here's 10 Brinker International SQL interview questions – can you answer each one?
Brinker International, operating several restaurant chains, wants to analyze its customer feedback data stored in a SQL database. In particular, they want to understand the average ratings customers have given to their different restaurants on a monthly basis.
Given below are two tables and . The table stores feedback submitted by customers with a unique , which it was for, , who submitted it and indicating the rating. The table details the , and .
review_id | user_id | submit_date | restaurant_id | stars |
---|---|---|---|---|
6171 | 123 | 2022-06-08 | 1 | 4 |
7802 | 265 | 2022-06-10 | 2 | 4 |
5293 | 362 | 2022-06-18 | 1 | 3 |
6352 | 192 | 2022-07-26 | 2 | 3 |
4517 | 981 | 2022-07-05 | 2 | 2 |
restaurant_id | restaurant_name | location |
---|---|---|
1 | Chili's | Dallas |
2 | Maggiano's Little Italy | Dallas |
In this query, we're using a window function to calculate the average () number of (ratings) per restaurant per month. The function is used to truncate the to the month. We are partitioning over both the truncated () and the which would help us find the average customer rating per restaurant per month. Finally, the results are ordered by and descending to understand better which restaurants performed best in terms of customer feedback each month.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
Suppose there was a table of Brinker International employee salary data, along with which department they belonged to. Write a query to compare the average salary of employees in each department to the company's average salary. 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.
Code your solution to this question interactively on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.
A cross-join, also known as a cartesian join, is like a mad scientist's laboratory experiment gone wild. It takes two tables and mixes them together to create a crazy new table with every possible combination of rows from the original tables.
Here's an example:
If you have 20 products and 10 colors, that's 200 rows right there! Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. Just like a mad scientist, use your powers wisely!
Brinker International owns several restaurant brands. They are interested in understanding the pattern of food orders in their restaurants across different locations. Specifically, they want to know which are the most ordered food items per month for each restaurant.
They operate with the following data tables:
restaurant_id | name | location |
---|---|---|
1 | Chili's | Dallas |
2 | Maggiano's | Las Vegas |
3 | Chili's | Sacramento |
item_id | item_name | restaurant_id |
---|---|---|
100 | Burger | 1 |
200 | Margarita | 1 |
300 | Spaghetti | 2 |
400 | Marsala Chicken | 2 |
500 | Tacos | 3 |
600 | Margarita | 3 |
order_id | item_id | order_date |
---|---|---|
111 | 100 | 2022-06-20 |
222 | 200 | 2022-06-23 |
333 | 300 | 2022-06-28 |
444 | 400 | 2022-07-02 |
555 | 500 | 2022-07-06 |
666 | 222 | 2022-07-08 |
Here is the question:
"What were the top-ordered food items at each restaurant per month?"
This SQL query first does a join on the , , and tables. It groups by month (extracted from ), , and (). Then it counts the number of orders for each group. After grouping and counting, it sorts the result by , , and in descending order. Finally, it limits the result to the top 1 to get the most ordered food item at each restaurant per month.
Please note, to get the top order for each restaurant, you would need to use a window function, which might be considered more advanced SQL. The provided query assumes simplicity in fetching top-ordered food items across all restaurants, not on a per-restaurant basis.
Both and are used to combine the results of two or more SELECT statements into a single result set.
However, only includes one instance of a duplicate, whereas includes duplicates.
Given our table for the Brinker International database, your task is to write an SQL query that will filter down the customer records to those who are living in , are 25 years old or above and have a valid membership.
customer_id | name | age | city | membership_status |
---|---|---|---|---|
1 | John Smith | 30 | Dallas | Valid |
2 | Lisa White | 24 | Dallas | Valid |
3 | Brian Green | 27 | Houston | Valid |
4 | Sara Brown | 40 | Dallas | Expired |
5 | David Jones | 32 | Dallas | Valid |
This will give us the filtered records of customers who are living in Dallas, are 25 years old or above and have a valid membership.
customer_id | name | age | city | membership_status |
---|---|---|---|---|
1 | John Smith | 30 | Dallas | Valid |
5 | David Jones | 32 | Dallas | Valid |
The output table shows customers who meet all the conditions which means they are above 25, from Dallas and have a valid membership.
Denormalization is the process of modifying a database schema in a way that deviates from the typical rules of normalization (1NF, 2NF, 3NF, etc.).
Denormalization is often used to improve the performance of a database, particularly when it is being used for reporting and analytical purposes (rather than in an Online Transaction Processing (OLTP) manager).
By duplicating data, denormalization can reduce the number of expensive joins required to retrieve data, which can improve query performance. However, denormalization can also cause problems such as increased data redundancy and the need for more complex update and delete operations.
Brinker International, Inc. is one of the world's leading casual dining restaurant companies. They own, operate, or franchise more than 1,600 restaurants under the names Chili's Grill & Bar and Maggiano's Little Italy. Let's say you are part of their data analysis team and you are asked to find the average daily sales for each restaurant in the month of October.
For this question, let's assume we have a 'sales' table with following structure:
sale_id | restaurant_id | sale_date | total_amount |
---|---|---|---|
1 | 100 | 10/01/2022 | 500.00 |
2 | 200 | 10/01/2022 | 700.00 |
3 | 100 | 10/02/2022 | 600.00 |
4 | 100 | 10/03/2022 | 450.00 |
5 | 200 | 10/03/2022 | 750.00 |
To solve this, we can group by restaurant ID and sale date, sum the total amount for each date, then find the average of these sums:
This query creates a subquery that sums daily sales for each restaurant, then calculates the average of these sums. Be careful to check the sale dates: we need to truncate the dates to months to make sure we only select dates in October. This query will return a list of average daily sales for each restaurant.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for measuring sales figures or this Amazon Average Review Ratings Question which is similar for utilizing time-based queries.
Brinker International, a multinational hospitality company, has been running several digital ad campaigns to attract more customers towards their food products. They track the data of customers visiting the product page through their ads (click-through) and those who further added the product to cart (conversion).
As a data analyst, you are tasked to calculate the click-through rate (the number of users who visited a productpage divided by the number of views the ad received) and the conversion rate (the number of users who added a product to the cart divided by the number of users who visited the product page) for their top running ad-campaign.
You have been provided with the following data tables:
ad_id | view_date | viewed_by_user |
---|---|---|
10 | 06/08/2022 00:00:00 | 123 |
10 | 06/10/2022 00:00:00 | 265 |
20 | 06/18/2022 00:00:00 | 362 |
10 | 07/26/2022 00:00:00 | 192 |
20 | 07/05/2022 00:00:00 | 981 |
ad_id | view_date | viewed_by_user |
---|---|---|
10 | 06/08/2022 00:00:00 | 123 |
10 | 06/10/2022 00:00:00 | 265 |
20 | 06/20/2022 00:00:00 | 362 |
10 | 07/26/2022 00:00:00 | 192 |
ad_id | add_date | added_by_user |
---|---|---|
10 | 06/08/2022 00:00:00 | 123 |
10 | 06/15/2022 00:00:00 | 265 |
20 | 06/22/2022 00:00:00 | 362 |
This query first calculates the click-through rate and then the conversion rate. It joins these two intermediary calculations on ad_id to present the final result. It's important to use DISTINCT when counting the users to avoid overcounting in case of repeat views or additions to the cart by the same user.
To solve a related problem on DataLemur's free interactive coding environment, try this Facebook SQL Interview question:
ACID refers to the four key properties that are essential to the reliable and correct execution of database transactions. These properties are:
Atomicity: ensures that a transaction is treated as a single operation, and either all of the changes are made or none of them are! Basically, the database version of a "-FULL SEND-"
Consistency: ensures that the data is in a consistent state before and after a transaction is completed. For example, if wiring money to a friendly Nigerian prince whose fallen on hard times, consistency ensures that the total value of funds lost in my account is the same amount that's gained in the prince's account!
Isolation: ensures that the intermediate state of a transaction is invisible to other transactions. Back to the wiring-the-prince-some-money example, isolation ensures that another transaction sees the transferred funds in my account OR the princes, but not in both accounts at the same time
Durability: ensures that once a transaction has been completed successfully, the changes made by the transaction are permanent and cannot be undone, even in the event of a system failure. Basically, no taksies backsies (even if your system has a meltdown!).
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Beyond just solving the above Brinker International SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Meta, Google and hospitality and restaurant companies like Brinker International.
Each interview question has multiple hints, full answers and crucially, there's an online SQL coding environment so you can right in the browser run your SQL query answer and have it checked.
To prep for the Brinker International SQL interview it is also a great idea to practice SQL problems from other hospitality and restaurant companies like:
In case your SQL coding skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers SQL topics like manipulating date/time data and SUM/AVG window functions – both of which show up often during Brinker International SQL interviews.
In addition to SQL interview questions, the other question categories to practice for the Brinker International Data Science Interview are:
To prepare for Brinker International Data Science interviews read the book Ace the Data Science Interview because it's got:
Don't ignore the behavioral interview – prep for that with this Behavioral Interview Guide for Data Scientists.