11 Rakuten SQL Interview Questions (Updated 2024)

At Rakuten, SQL is used often for analyzing customer behavior patterns to improve marketing efficiency, and managing company-wide data to monitor e-commerce trends. That's why Rakuten almost always evaluates jobseekers on SQL problems during interviews for Data Science and Data Engineering positions.

So, to help you ace the Rakuten SQL interview, we've curated 11 Rakuten SQL interview questions – can you solve them?

11 Rakuten SQL Interview Questions

SQL Question 1: Identify VIP Customers within Rakuten's ecosystem

Rakuten is an international online retailer with operations in many countries. For this SQL interview task, assume Rakuten maintains two tables in its database - 'orders' and 'customers'. The 'orders' table stores the order details of all purchases done by customers and the 'customers' table holds all the information about Rakuten's customers.

The 'orders' table has the following column definitions:

• order_id (integer): A unique identifier of each order placed by customers
• user_id (integer): The unique identifier of the customer who placed the order (in sync with the ID in the 'customers' table)
• order_amount (double): The total amount of the order
• order_date (timestamp): The order creation date

The 'customers' table has the following column definitions:

• user_id (integer): A unique identifier of each customer
• user_name (string): The name of the customer

Your task is to write a SQL query that identifies all 'VIP' users for Rakuten. Rakuten defines a VIP user as one who has provided the business with a sum of order amounts exceeding $100,000 during the last 365-day period.

Example Input:

Example Input:

Answer:

This query first finds all the customers who have an order amount sum more than $100,000 for the last 365 period from the 'orders' table. Then it joins this output with the 'customers' table to retrieve the user names of the VIP customers. If any user_id in the 'orders' table doesn't exist in the 'customers' table, that user wouldn't be selected because of the INNER JOIN operation.

To practice a similar power-user data analysis problem question on DataLemur's free online SQL code editor, try this recently asked Microsoft SQL interview question:

SQL Question 2: Calculate Monthly Average Rating for Each Product

For Rakuten, an eCommerce company, understanding the rating patterns for its products is crucial. Write a SQL query to calculate the monthly average rating for each product.

The sample data from the table includes (unique identifier of the review), (identifier of the user who submitted the review), (the date when the review was submitted in 'mm/dd/yyyy hh24:mi:ss' format), (identifier of the product) and (rating submitted by the user on a scale of 1 to 5).

Example Input:

Answer:

This query extracts the month from the and groups the reviews by month and . It then calculates the average for each group.

Example Output:

For more window function practice, solve this Uber SQL Interview Question within DataLemur's online SQL code editor:

SQL Question 3: Why should you normalize your database?

There are several advantages to normalizing a database, including less redundancy, more flexibility, and better performance.

• Less Redundancy: Normalization reduces redundancy by breaking down a larger, more general table into smaller, more specific tables. This reduces the amount of data that needs to be accessed for queries.

• More Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (a very real reality at Rakuten!)

• Better Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This can result in faster query times and better overall performance.

SQL Question 4: Analyze Product Category Performance

As a data analyst at Rakuten, an e-commerce company, you are asked to analyze the performance of various product categories over time. Rakuten has a diverse range of products categorized under different sections. The aim is to find out the most popular categories each month based on the quantity sold. You also need to figure out the total revenue generated by each category in the same time period.

Consider the following tables

Example Input:

Example Input:

Answer:

The following SQL query using PostgreSQL syntax can achieve this:

This query first joins the sales and products table using the product_id as the common key. It groups the result by the month of the sale date and the category_id. The function is used to extract the month from the sale_date. It aggregates the quantity_sold to give the total_products_sold and also calculates total_revenue by multiplying quantity_sold with respective product price. The resulting data is then ordered by the month, the total product sold in descending order and if there is a tie, the total revenue in descending order.

The output will be a table with columns - month, category_id, total_products_sold and total_revenue.

SQL Question 5: In the context of databases, what does atomicity, consistency, isolation, and durability mean?

A transaction is a one or more SQL commands which are executed as a singular unit if the transaction -commits- (or no execution hapens if the transaction -aborts-).

For transactions, a DBMS is supposed to enforce the follwing ACID properties: Atomicity, Consistency, Isolation, & Durability.

Here's what each one means:

• Atomicity: the transaction is completed in an all-or-nothing way (no partial commits)
• Consistency: the transaction is valid and follows all constraints and restrictions
• Isolation: the transaction doesn't affect another transaction
• Durability: the committed transactions is stored permanently in the DB (it doesn't dissapear!)

As you can see, it's pretty important for the multiple databases where Rakuten store's it's data to be ACID-compliant!

SQL Question 6: Filter Customers Based on Purchasing Habits

Rakuten is a global e-commerce company and it has a large database of customers. The company wants to run a targeted campaign for each customer segment. As a part of the campaign, the company wants to filter out customers who purchase in all 4 quarters of the year and spend more than $1000 in each quarter. Consider that each order is treated as one transaction and a customer can make multiple transactions in a quarter.

Assume you have an database with the following schema:

Example Input:

Write a PostgreSQL query to filter out the customers who meet the above conditions.

Answer:

Here is the SQL query:

This query first groups the data by and then counts the distinct number of quarters and sums up the for each customer. The clause is then used to filter out customers who made purchases in all 4 quarters and spent at least $1000 in each quarter. We multiply by year and quarter to ensure that quarters from different years are recognized as different. The greater than or equal to sum of purchase amount is set to 4000, considering that we want the customers who spend more than$1000 in each of the four quarters.

SQL Question 7: Can you describe the concept of database denormalization in layman's terms?

Database denormalization is when you add redundancy to a database, and break typical normalization rules (codified by the 1st, 2nd, 3rd normal forms).

Denormalization is typically used to improve the performance of a database, particularly when the database is being for OLAP (Online Analytical Processing) purposes. By denormalizing a database, you can reduce the number of joins that are required to retrieve data, which can greatly improve the speed of queries since joins are costly and slow. However, denormalization can also introduce some problems, such as increased data redundancy and the need for more complex update and delete operations since data has been duplicated into multiple tables.

In general, denormalization should be used with caution and only after careful consideration of the trade-offs involved. It's typically better to start with a well-normalized database design then denormalize only if your database isn't scaling as well as you want.

SQL Question 8: Calculate Click-Through-Rate on E-commerce Site

Rakuten is a famous ecommerce platform where millions of users visit every day to buy various products. On Rakuten, when a user clicks on a product, they view the product details and then possibly add the product to their cart. For Rakuten, it is really important to monitor the Click-Through-Rate (CTR) from viewing a product to adding a product to the cart.

Let's say the table looks like this:

example data:

And the table looks like this:

example data:

Please write a SQL query to calculate the daily click-through-rate (CTR) of each product_id in the month of June, 2022.

Answer:

Here's a sample SQL query to calculate the Click-Through-Rate (CTR) in PostgreSQL.

This query calculates the by first finding the total views and adds for each product and day, then dividing the count of adds to the cart by the count of product views. Note that you might get for if there were views but no adds on a particular day for a product. You can handle these according to your business requirements.

To practice a related SQL problem on DataLemur's free interactive coding environment, solve this Facebook SQL Interview question:

SQL Question 9: Find the Highest Selling Category

Rakuten, being an e-commerce platform, sells a range of products categorized into different categories. We are interested in knowing which category made the highest total sales in the year 2022. Your job is to write a SQL query that outputs the highest selling category's name along with the total sales.

Sample data:

:

:

:

Answer:

The above query will present the category name with the highest total sales in the year 2022. The query joins the 'categories', 'products', and 'orders' tables together to gather all the necessary information, grouping by 'category_name' and summing up the quantities sold. It orders the output by 'total_sales' in descending order and limits the result to show only the highest selling category.

SQL Question 10: Can you provide an example of two entities that have a one-to-one relationship, and another example of a one-to-many relationship?

When designing a database schema, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a car and a license plate - each car has one license plate, and each license plate belongs to one car.

On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. For example, a person can have multiple email addresses, but each email address only relates back to one person.

SQL Question 11: Searching Customer Email Domains

As a data analyst at Rakuten, we are given the task of finding all the customer records from the customers database whose email addresses are registered with a certain domain. For example, let's say we want all customers whose emails are registered with 'gmail.com'. Write a SQL query to solve this issue.

Here is the input table for this problem:

Example Input:

The output of the query should be:

Example Output:

Answer:

To get the data for customers with a 'gmail.com' domain, we can write the following SQL query:

This query uses the SQL keyword, which allows us to filter rows that match a specified pattern. The symbol in the pattern is a wildcard character that can match any sequence of characters. Here it is used to match any email address that ends with '@gmail.com'. The result is the rows of customers whose email is registered with 'gmail.com'.

Preparing For The Rakuten SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Rakuten SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Rakuten SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Netflix, Google, and Amazon.

Each interview question has hints to guide you, full answers and most importantly, there's an interactive coding environment so you can easily right in the browser your SQL query answer and have it graded.

To prep for the Rakuten SQL interview you can also be useful to practice SQL problems from other tech companies like:

• Amazon SQL questions

• zozo SQL questions

• eBay SQL questions

• Etsy SQL questions

But if your SQL foundations are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers SQL concepts such as AND/OR/NOT and GROUP BY – both of these pop up routinely in Rakuten interviews.

Rakuten Data Science Interview Tips

What Do Rakuten Data Science Interviews Cover?

In addition to SQL interview questions, the other types of problems covered in the Rakuten Data Science Interview are:

• Statistics & Probability Questions
• Python or R Coding Questions
• Product Metrics Interview Questions
• Machine Learning and Predictive Modeling Questions
• Behavioral & Resume-Based Questions

How To Prepare for Rakuten Data Science Interviews?

To prepare for Rakuten Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions sourced from FAANG, tech startups, and Wall Street
• a crash course on Python, SQL & ML
• over 900+ reviews on Amazon & 4.5-star rating