At IONOS Group, SQL is often used for analyzing customer data to enhance product offerings and managing databases to ensure optimal data infrastructure and data integrity. Unsurprisingly this is why IONOS asks SQL coding questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
To help you study for the IONOS SQL interview, we've curated 9 IONOS Group SQL interview questions – how many can you solve?
IONOS has a product line, and customers frequently submit their reviews about them. As a Data Scientist at IONOS, you've been tasked to analyze these reviews to understand how the average rating of each product changes on a monthly basis. You have been given a dataset containing customer review details.
Your task is to write a SQL query that calculates the average rating for each product per month. You need to use PostgreSQL for this task.
Consider the following data from table:
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 |
Here is a PostgreSQL query to solve the problem:
This query uses the function to get the month from the column, and leverages the window function with the clause to calculate the average rating for each product per month. The clause is used to arrange the results by month and product.
Here's a table that represents how the output might look:
mth | product | avg_stars |
---|---|---|
6 | 50001 | 3.5 |
6 | 69852 | 4.0 |
7 | 69852 | 2.5 |
This table shows the average stars for each product on a monthly basis. From this table, it can be inferred that product has an average of stars in June, product has an average of stars in June, and stars in July.
For more window function practice, solve this Uber SQL problem within DataLemur's online SQL code editor:
IONOS is a leading web hosting and cloud services company. One of their most important tasks is managing the storage allocated to each customer. The challenge is to determine the average storage utilization per customer over the 12-month period.
customer_id | month | year | storage_GB |
---|---|---|---|
1012 | 1 | 2022 | 10 |
1012 | 2 | 2022 | 12 |
1013 | 1 | 2022 | 15 |
1013 | 2 | 2022 | 18 |
1014 | 1 | 2022 | 8 |
1014 | 2 | 2022 | 12 |
Your task is to write a PostgreSQL query that calculates the average storage utilized by each customer over the year 2022.
This query groups the storage_usage table by customer_id, and for each customer, it calculates the average of the storage_GB column where year equals 2022. This will give us the average storage utilized by each customer over the year 2022. The AVG function calculates the average of a set of values by adding up all the values and then dividing by the count.
Think of SQL constraints like the rules of a game. Just like a game needs rules to keep things fair and fun, a database needs constraints to keep things organized and accurate.
There are several types of SQL constraints like:
NOT NULL: This constraint is like a bouncer at a nightclub - it won't let anything NULL through the door. UNIQUE: This constraint is like a VIP list - only special, one-of-a-kind values get in. PRIMARY KEY: This constraint is like an elected official - it's made up of NOT NULL and UNIQUE values and helps identify each row in the table. FOREIGN KEY: This constraint is like a diplomatic ambassador - it helps establish relationships between tables. CHECK: This constraint is like a referee - it makes sure everything follows the rules. DEFAULT: This constraint is like a backup plan - it provides a default value if no other value is specified.
So, whether you're playing a game or organizing a database, constraints are an important part of the process!
IONOS, providing domains, website and email services chiefly, needs to understand how their different product categories are performing in terms of sales. Can you help them by writing a query that provides the average revenue per category for each month?
sales_id | transaction_date | product_category | product_id | revenue |
---|---|---|---|---|
1 | 01/01/2022 | Domains | 101 | 100 |
2 | 01/01/2022 | Website | 201 | 200 |
3 | 01/01/2022 | 301 | 50 | |
4 | 02/01/2022 | Domains | 102 | 150 |
5 | 02/01/2022 | Website | 202 | 250 |
6 | 02/01/2022 | 302 | 75 |
month | product_category | avg_revenue |
---|---|---|
1 | Domains | 100 |
1 | Website | 200 |
1 | 50 | |
2 | Domains | 150 |
2 | Website | 250 |
2 | 75 |
This query extracts the month from the transaction date and groups by both the month and the product category. It calculates the average revenue by category for each month. The ORDER BY clause ensures that the results are returned in order of month and product category.
An inner join returns only the rows that match the join condition between the two tables, whereas a full outer join returns all rows from both tables, including any unmatched rows.
Example:
Example:
As a company, IONOS provides a variety of hosting and cloud services including domain names. Given the customer database, your task is to write a query to find all the customers who have registered a domain name that includes '.com'. For simplicity, let's assume IONOS would register domains for customers under column domain_name.
Our customer records example input would look something like this:
customer_id | first_name | last_name | domain_name |
---|---|---|---|
5171 | John | Doe | johndoe.com |
2802 | Mary | Smith | marysmith.org |
2931 | Robert | Brown | rbrown.com |
3523 | Tom | Wilson | twilson.net |
8817 | Emily | Clark | emilyclark.com |
Your output should return all the customers who have a '.com' domain.
customer_id | first_name | last_name | domain_name |
---|---|---|---|
5171 | John | Doe | johndoe.com |
2931 | Robert | Brown | rbrown.com |
8817 | Emily | Clark | emilyclark.com |
This postgresql query uses the 'LIKE' keyword to match any records in the 'domain_name' column that contains '.com'. The percent sign (%) in the pattern indicates any number of characters (including zero characters) between the characters before and after the percent sign. Since we are looking for '.com' anywhere in the 'domain_name' column, we enclose '.com' in two percent signs. This query will show the details of customers who have registered a '.com' domain.
Before we address vs. question, let's address the clause which is used in a statement to group rows into a set of summary rows. It's typically used in conjunction with aggregate functions such as:
The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
For example:
This query retrieves the total salary for each department and groups the rows by department. The clause then filters the groups to include only IONOS departments where the total salary is greater than $1 million
Note that the clause must be used after the clause and can only reference columns listed in the clause or aggregated columns selected by the statement.
Okay, here's a hypothetical SQL join question:
IONOS is interested in analyzing its customer database to understand their average monthly billing amounts. They have two tables, "customers" and "billings".
The "customers" table has the following columns: customer_id, first_name, last_name, email, and signup_date.
The "billings" table has the following columns: bill_id, customer_id, bill_date, amount.
Please write a SQL query that joins these tables and calculates average monthly billing per customer.
customer_id | first_name | last_name | signup_date | |
---|---|---|---|---|
1 | John | Doe | johndoe@email.com | 2022-01-01 |
2 | Jane | Smith | janesmith@email.com | 2021-12-01 |
3 | Bob | Johnson | bobjohnson@email.com | 2022-02-01 |
bill_id | customer_id | bill_date | amount |
---|---|---|---|
1 | 1 | 2022-02-01 | 99.99 |
2 | 1 | 2022-03-01 | 149.99 |
3 | 2 | 2022-02-01 | 49.99 |
4 | 2 | 2022-03-01 | 49.99 |
5 | 3 | 2022-03-01 | 79.99 |
The solution to this problem involves joining the two tables on the "customer_id" column and then grouping by the customer and the month/year of the billing date.
Here's the SQL query that performs this calculation.
This query first joins the two tables on the customer_id. Then it groups the results by the customer and the billing month. The function is used to truncate the date to the nearest month. Finally, it calculates the average billing amount for each customer for each month.
Because joins come up so often during SQL interviews, try an interactive SQL join question from Spotify:
IONOS provides a variety of web hosting services. For this use case, let's assume that we want to know the average daily usage in a given month for each hosting service package. You are given a PostgreSQL database with a table named that records every time a customer uses a hosting service. The structure of the table is as follows:
log_id | customer_id | usage_date | service_package | usage_hours |
---|---|---|---|---|
6151 | 321 | 01/05/2022 00:00:00 | Basic | 2 |
7022 | 562 | 01/15/2022 00:00:00 | Premium | 6 |
5893 | 362 | 01/16/2022 00:00:00 | Basic | 1 |
6852 | 212 | 01/18/2022 00:00:00 | Advanced | 3 |
5587 | 931 | 01/20/2022 00:00:00 | Advanced | 5 |
Write a SQL query that, for each service package, returns the month and the average daily usage of the service in that month.
month | service_package | avg_daily_usage |
---|---|---|
1 | Basic | 1.50 |
1 | Premium | 6.00 |
1 | Advanced | 4.00 |
This query utilizes the function to get the month from the column. Afterward, function is utilized to calculate the average daily usage for each service package. The clause groups the results by the month and the service package.
The best way to prepare for a IONOS SQL interview is to practice, practice, practice. Beyond just solving the earlier IONOS SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Facebook, Google, and VC-backed startups.
Each exercise has multiple hints, step-by-step solutions and crucially, there is an online SQL code editor so you can easily right in the browser your SQL query and have it checked.
To prep for the IONOS SQL interview you can also be a great idea to practice SQL questions from other tech companies like:
In case your SQL skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this free SQL tutorial.
This tutorial covers topics including using ORDER BY and handling dates – both of which show up often in IONOS interviews.
In addition to SQL query questions, the other types of questions tested in the IONOS Data Science Interview are:
To prepare for IONOS Data Science interviews read the book Ace the Data Science Interview because it's got: