At HelloFresh, SQL is typically used for analyzing customer behavior trends and optimizing logistics for fresh food delivery. That's why HelloFresh frequently asks SQL query questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
So, to help you practice for the HelloFresh SQL interview, we've curated 11 HelloFresh SQL interview questions – able to answer them all?
HelloFresh is a meal kit delivery service where customers can order the number of meals they need every week. To understand better about the power users, or VIP users, we want to measure the frequency that a user orders meal kits.
The task is to write a SQL query to find the top 5 users who ordered the meal kits the highest number of times within the month of August 2022.
Assume we have a table orders
with the following schema:
orders
Example Inputorder_id | user_id | order_date | meal_id | quantity |
---|---|---|---|---|
15432 | 789 | 08/17/2022 00:00:00 | 60009 | 3 |
17386 | 654 | 08/25/2022 00:00:00 | 70123 | 2 |
14952 | 761 | 08/02/2022 00:00:00 | 60009 | 4 |
16752 | 876 | 08/15/2022 00:00:00 | 70123 | 3 |
14471 | 654 | 08/18/2022 00:00:00 | 70123 | 1 |
Here is the SQL query that solves this problem:
SELECT user_id, COUNT(order_id) as order_count FROM orders WHERE DATE_PART('month', order_date) = 8 AND DATE_PART('year', order_date) = 2022 GROUP BY user_id ORDER BY order_count DESC LIMIT 5;
This query starts by filtering out the orders that were placed within the month of August 2022. Then it counts the number of orders grouped by each user. The query finally returns the top 5 users that ordered the most number of times, in a descending order.
To practice a super-customer analysis question on DataLemur's free online SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
At HelloFresh, we take our delivery time seriously; both to ensure a good customer experience and to keep the ingredients fresh. We want to determine the average delivery time for each recipe.
For this question, assume we have a "deliveries" table that shows when a recipe was prepared and when it was delivered to the customer. The aim is to write a SQL query to calculate the average delivery time for each recipe.
deliveries
Example Input:delivery_id | recipe_id | recipe_name | prepared_time | delivered_time |
---|---|---|---|---|
1 | 10 | Chicken Curry | 06/07/2022 09:00:00 | 06/07/2022 13:00:00 |
2 | 20 | Vegetable Stir Fry | 06/07/2022 09:30:00 | 06/07/2022 13:30:00 |
3 | 10 | Chicken Curry | 06/08/2022 09:00:00 | 06/08/2022 12:30:00 |
4 | 30 | Pasta Bolognese | 06/09/2022 09:00:00 | 06/09/2022 12:00:00 |
5 | 20 | Vegetable Stir Fry | 06/09/2022 09:30:00 | 06/09/2022 13:00:00 |
recipe_id | recipe_name | avg_delivery_time_hours |
---|---|---|
10 | Chicken Curry | 3.75 |
20 | Vegetable Stir Fry | 4.00 |
30 | Pasta Bolognese | 3.00 |
SELECT recipe_id, recipe_name, AVG(EXTRACT(EPOCH FROM (delivered_time - prepared_time))/3600) AS avg_delivery_time_hours FROM deliveries GROUP BY recipe_id, recipe_name;
The SELECT statement is asking for the recipe_id and recipe_name columns from the "deliveries" table. The AVG clause calculates the average delivery time, which is calculated as the time difference between delivered_time and prepared_time, converted to hours using the EXTRACT(EPOCH FROM INTERVAL) function to get seconds and divided by 3600. This is then grouped by recipe_id and recipe_name, providing the average delivery time for each recipe.
To practice a similar window function question on DataLemur's free online SQL coding environment, solve this Google SQL Interview Question:
FOREIGN KEY
constraint?A FOREIGN KEY
is a field in a table that references the PRIMARY KEY
of another table. It creates a link between the two tables and ensures that the data in the FOREIGN KEY
field is valid.
Say for example you had sales analytics data from HelloFresh's CRM (customer-relationship management) tool.
CREATE TABLE hellofresh_accounts ( account_id INTEGER PRIMARY KEY, account_name VARCHAR(255) NOT NULL, industry VARCHAR(255) NOT NULL ); CREATE TABLE opportunities ( opportunity_id INTEGER PRIMARY KEY, opportunity_name VARCHAR(255) NOT NULL, account_id INTEGER NOT NULL, FOREIGN KEY (account_id) REFERENCES hellofresh_accounts(account_id) );
The FOREIGN KEY constraint ensures that the data in the account_id
field of the "opportunities" table is valid, and prevents the insertion of rows in the opportunities
table that do not have corresponding entries in the hellofresh_accounts
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 accounts table if there are still references to it in the opportunities` table.
HelloFresh, an international meal-kit company, delivers pre-portioned ingredients and step-by-step recipes directly to customers. They operate a number of warehouses where they store ingredients before they are packed into meal kits.
HelloFresh needs to carefully track their ingredients inventory across these warehouses to ensure that they can fulfill customer orders.
The schema you are provided with contains three tables:
The products
table tracks unique meal-kit products that HelloFresh offers. The product_id
is a unique identifier for each meal-kit product.
The ingredients
table tracks unique ingredients used in their meal-kits. The ingredient_id
is a unique identifier for each.
The product_ingredients
table represents a many-to-many relationship between products
and ingredients
. Each row represents that an ingredient is used in a certain product, and has a quantity
indicating the amount of the ingredient used in that product.
The inventory
table keeps track of the quantity of each ingredient available in each warehouse.
The database schema is as follows:
products
product_id | name | description |
---|---|---|
50001 | 'Chicken Alfredo Pasta' | 'Classic chicken Alfredo served over pasta.' |
69852 | 'Thai Basil Tofu' | 'Spicy Thai tofu dish served with Jasmine rice.' |
ingredients
ingredient_id | name | unit |
---|---|---|
25 | 'Chicken Breast' | 'piece' |
36 | 'Pasta' | 'g' |
42 | 'Alfredo Sauce' | 'ml' |
30 | 'Tofu' | 'g' |
37 | 'Thai Basil' | 'leave' |
40 | 'Jasmine Rice' | 'g' |
product_ingredients
product_id | ingredient_id | quantity |
---|---|---|
50001 | 25 | 1 |
50001 | 36 | 200 |
50001 | 42 | 50 |
69852 | 30 | 150 |
69852 | 37 | 20 |
69852 | 40 | 75 |
inventory
warehouse_id | ingredient_id | quantity_on_hand |
---|---|---|
1 | 25 | 500 |
1 | 36 | 50000 |
1 | 42 | 2500 |
2 | 30 | 2000 |
2 | 37 | 1000 |
2 | 40 | 10000 |
Your challenge is to write a SQL query that returns the total number of each product that can currently be made in each warehouse, given the quantities of ingredients available.
SELECT warehouse_id, products.product_id, MIN(inventory.quantity_on_hand::real / product_ingredients.quantity) AS total_product_can_make FROM inventory JOIN product_ingredients ON inventory.ingredient_id = product_ingredients.ingredient_id JOIN products ON products.product_id = product_ingredients.product_id GROUP BY warehouse_id, products.product_id ORDER BY total_product_can_make DESC;
This SQL query joins the inventory
, products
and product_ingredients
tables through their relationships, using product and ingredient IDs. For each group of warehouses and products, the query calculates the minimum ratio of quantities on hand and the quantities required for the product. This represents the number of product kits that could be made with the available inventory. The final results are sorted by the total number of product kits that can be made in descending order.
Database denormalization is when you add redundancy to a database, and break typical normalization rules (specified by 1st, 2nd, 3rd normal forms). There's a few reasons to denormalize a database:
Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases, as joins can be expensive and slow.
Simplification: Denormalization can also be used to simplify the design of a database by reducing the number of tables and relationships that need to be managed. This can make it easier to understand and maintain the database.
Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.
While denormalization can be a useful tool for improving performance and scalability, it's important to keep in mind that it can make update and delete operations more complex. This is because denormalization can create data duplicates, which can make it harder to maintain data integrity. So, to avoid any headaches, it's a good idea to start with a well-normalized database design and then consider denormalization only if it's absolutely necessary for your specific performance and scalability needs. In other words, denormalization can be a bit of a wild card, so it's best to handle it with care!
Consider a database for HelloFresh. You are given two tables:
customers
, where each row represents a unique customer, andorders
, where each row represents an order made by a customer.Table customers
:
customer_id | first_name | last_name | signup_date | is_active |
---|---|---|---|---|
1 | John | Doe | 01/02/2019 | True |
2 | Jane | Doe | 05/03/2020 | True |
3 | Bob | Alice | 12/15/2020 | False |
4 | Charlie | Fox | 07/04/2021 | True |
5 | David | Universe | 11/20/2019 | False |
Table orders
:
order_id | customer_id | order_date | meals_number |
---|---|---|---|
1 | 1 | 01/07/2019 | 3 |
2 | 1 | 01/14/2019 | 2 |
3 | 2 | 05/09/2020 | 4 |
4 | 2 | 05/16/2020 | 3 |
5 | 3 | 12/21/2020 | 3 |
6 | 4 | 07/10/2021 | 2 |
7 | 5 | 12/01/2019 | 2 |
Write a PostgreSQL query that returns the full name and signup date of customers who are currently active and have placed an order of at least 3 meals or more in the past.
SELECT c.first_name || ' ' || c.last_name AS full_name, c.signup_date FROM customers c JOIN orders o ON c.customer_id = o.customer_id WHERE c.is_active = True AND o.meals_number >= 3;
This query joins the customers table with the orders table on the customer_id
field, then filters for only customers who are active (is_active = True
) and have placed an order with 3 or more meals in the past (meals_number >= 3
). The result is a list of full names and signup dates of these customers.
To ensure the reliability and integrity of data, a database management system (DBMS) strives to maintain the ACID properties: Atomicity, Consistency, Isolation, and Durability. To illustrate these concepts, consider the following examples of how ACID properties apply to banking transactions:
As a part of the Data Analyst team at HelloFresh, your task is to find the average cost of meals provided for different cuisines. The company is particularly interested in knowing if there's a significant price difference among different cuisines. Use the following tables meals
and cuisines
.
meals
Example Input:meal_id | cuisine_id | price |
---|---|---|
101 | 1 | 10.99 |
102 | 1 | 11.99 |
103 | 2 | 12.99 |
104 | 3 | 15.99 |
105 | 2 | 13.99 |
cuisines
Example Input:cuisine_id | cuisine_name |
---|---|
1 | Italian |
2 | Mexican |
3 | Indian |
cuisine | average_price |
---|---|
Italian | 11.49 |
Mexican | 13.49 |
Indian | 15.99 |
SELECT C.cuisine_name AS cuisine, AVG(M.price) AS average_price FROM meals M JOIN cuisines C ON M.cuisine_id = C.cuisine_id GROUP BY C.cuisine_name;
In the solution, we are joining meals and cuisines tables on the common column cuisine_id
. After the join, we group the data on the basis ofcuisine_name
and calculate the average price for each cuisine using the AVG function. The results are aliases as cuisine
and average_price
for readability.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring aggregation for category-like data or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for drawing conclusions from product and profit data.
HelloFresh has a database of customers, and they would like to retrieve information of customers with email ids that contain 'gmail'. Could you generate a SQL query that performs this operation?
customers
Example Input:customer_id | first_name | last_name | email_id |
---|---|---|---|
6171 | John | Doe | john.doe@gmail.com |
7802 | Jane | Smith | jane.smith@yahoo.com |
5293 | Sarah | Johnson | sarah.johnson@hotmail.com |
6352 | Robert | Brown | robert.brown@gmail.com |
4517 | Linda | White | linda.white@gmail.com |
customer_id | first_name | last_name |
---|---|---|
6171 | John | Doe |
6352 | Robert | Brown |
4517 | Linda | White |
Here is a SQL query that will do this:
SELECT customer_id, first_name, last_name FROM customers WHERE email_id LIKE '%gmail%';
This query works by selecting the customer_id
, first_name
, and last_name
columns from the customers
table where the email_id
contains the term 'gmail'. The '%' character in the LIKE keyword is a wild card that matches any sequence of characters, therefore '%gmail%' will match any email id that contains 'gmail'. This is useful to retrieve information about customers that have a Gmail account.
INTERSECT
do, and when would you use this SQL command?When using INTERSECT
, only rows that are identical in both sets will be returned.
For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at HelloFresh, and data on potential sales leads lived in both Salesforce and Hubspot CRMs. To write a query to analyze leads created before 2023 started, that show up in BOTH CRMs, you would use the INTERSECT
command:
SELECT email, job_title, company_id FROM hellofresh_sfdc_leads WHERE created_at < '2023-01-01'; INTERSECT SELECT email, job_title, company_id FROM hellofresh_hubspot_leads WHERE created_at < '2023-01-01'
Using the customers
and meals
tables, your task is to write a query that calculates the most popular meal option sold each month.
customers
Sample Input:customer_id | join_date | country |
---|---|---|
4231 | 05/10/2021 | USA |
9350 | 03/25/2021 | USA |
2175 | 07/02/2021 | Germany |
9873 | 04/15/2021 | France |
3221 | 08/30/2021 | USA |
meals
Sample Input:meal_id | customer_id | purchase_date | meal |
---|---|---|---|
1057 | 4231 | 05/12/2021 | Chicken Alfredo |
2049 | 9350 | 03/28/2021 | Vegetable Lasagna |
7396 | 2175 | 07/04/2021 | German Bratwurst |
6284 | 9873 | 04/18/2021 | French Coq au Vin |
4865 | 3221 | 09/01/2021 | Chicken Caesar Salad |
Your query should return the most popular meal option sold each month, for all customers regardless of the country, as the following table:
year_month | top_meal | meal_count |
---|---|---|
2021-03 | Vegetable Lasagna | 1 |
2021-04 | French Coq au Vin | 1 |
2021-05 | Chicken Alfredo | 1 |
2021-07 | German Bratwurst | 1 |
2021-09 | Chicken Caesar Salad | 1 |
In this question, we need to join the customers
and meals
tables on their common customer_id
column, group by the year and month of the purchase, and then count the meals to find out which is most popular each month.
Here's the SQL query for this:
SELECT DATE_TRUNC('month', m.purchase_date) AS year_month, m.meal AS top_meal, COUNT(m.meal) AS meal_count FROM customers c JOIN meals m ON c.customer_id = m.customer_id GROUP BY year_month, top_meal ORDER BY year_month, meal_count DESC;
In the above SQL query, DATE_TRUNC('month', m.purchase_date)
takes the purchase date and truncates this to the year-month, so all days within the same month and year are grouped together. COUNT(m.meal)
simply counts how many of each meal were sold. The ORDER BY
at the end ensures that the data is sorted by month first, and then by meal count in descending order, so the most popular meal each month appears first.
Because join questions come up so often during SQL interviews, practice an interactive Spotify JOIN SQL question:
The best way to prepare for a HelloFresh SQL interview is to practice, practice, practice. Besides solving the earlier HelloFresh SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Netflix, Airbnb, and Amazon.
Each SQL question has hints to guide you, full answers and best of all, there's an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it executed.
To prep for the HelloFresh SQL interview you can also be helpful to practice SQL problems from other tech companies like:
And if you're curious about how Data Science is used at these companies check out these blogs!
But if your SQL coding skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers topics including handling NULLs in SQL and SQL joins with practice exercises – both of which come up routinely in SQL job interviews at HelloFresh.
In addition to SQL interview questions, the other question categories covered in the HelloFresh Data Science Interview are:
To prepare for HelloFresh Data Science interviews read the book Ace the Data Science Interview because it's got: