8 W.W. Grainger SQL Interview Questions (Updated 2024)
At W.W. Grainger, SQL is used for analyzing sales trends to identify which products are gaining popularity so they can adjust their marketing strategies. They also use it manages inventory data, ensuring they have the right amount of stock to meet their distribution and industrial supply needs, this is the reason why W.W. Grainger evaluates jobseekers with SQL problems in interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, to help you ace the W.W. Grainger SQL interview, we'll cover 8 W.W. Grainger SQL interview questions in this article.
8 W.W. Grainger SQL Interview Questions
SQL Question 1: Identify Top Buyers from W.W. Grainger
W.W. Grainger is a leading business-to-business distributor that sells products in categories like safety, material handling and metalworking, along with services like inventory management and technical support. An example of a substantial activity for this business is the purchase of products.
First, define a power user for Grainger as a customer that places top 5% of the orders in terms of quantity. You need to provide a list of these power users with their total purchases within the last year.
Example Input:
| order_id | customer_id | order_date | product_id | quantity |
|---|---|---|---|---|
| 1 | 453 | 2021-10-01 | 1234 | 5 |
| 2 | 789 | 2021-11-01 | 5678 | 10 |
| 3 | 453 | 2021-11-02 | 1234 | 6 |
| 4 | 998 | 2021-12-20 | 7890 | 7 |
| 5 | 453 | 2022-01-25 | 5678 | 5 |
Example Input:
| customer_id | name |
|---|---|
| 453 | ABC Corp |
| 789 | XYZ Corp |
| 998 | DEF Corp |
Answer:
The above query first calculates the total quantity of purchases made by each customer within the last year. It then calculates the 95th percentile of these total quantities to define the cutoff for being a power user. The final query fetches the names of customers whose total purchases are above this cutoff along with their total purchases, identifying the power users.
To solve a super-customer analysis question on DataLemur's free online SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
Stay updated with W.W. Grainger's press releases to learn about their latest initiatives and innovations! Keeping track of their developments can offer insights into how they are addressing industry challenges and enhancing their service offerings.
SQL Question 2: Top Three Salaries
Imagine there was a table of W.W. Grainger employee salary data. Write a SQL query to find the top 3 highest earning employees in each department.
W.W. Grainger Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
You can solve this interview question and run your code right in DataLemur's online SQL environment:
Answer:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the solution above is confusing, you can find a detailed solution here: Top 3 Department Salaries.
SQL Question 3: Can you explain the concept of database normalization?
Normalization involves breaking up your tables into smaller, more specialized ones and using primary and foreign keys to define relationships between them. Not only does this make your database more flexible and scalable, it also makes it easier to maintain. Plus, normalization helps to keep your data accurate by reducing the chance of inconsistencies and errors.
The only downside is now is that your queries will involve more joins, which are slow AF and often a DB performance botteleneck.
SQL Question 4: Calculate monthly average product review scores
Given the data on product reviews, can you write a SQL query using the window function to calculate the average rating of each product by month?
In this context, each row in the table represents a separate review for a product. The columns include an identifier for the review (), the user who submitted the review (), the date the review was submitted (), the identifier for the product (), and the number of stars given to the product ().
Example Input:
| 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 |
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
This query calculates the average for each by . It uses the window function in combination with to calculate the average rating over each and month group. The results are sorted by and .
For more window function practice, try this Uber SQL Interview Question within DataLemur's online SQL code editor:
SQL Question 5: What's a correlated sub-query? How does it differ from a non-correlated sub-query?
A correlated sub-query is a sub-query that depends on the outer query and cannot be executed separately. It uses the outer query to filter or transform data by referencing a column from it, and the outer query uses the results of the inner query. In contrast, a non-correlated sub-query is independent of the outer query and can be executed on its own. It does not reference any columns from the outer query and is used to retrieve additional data needed by the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all W.W. Grainger customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Performance-wise, correlated sub-queries are generally slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
SQL Question 6: Filter Customers Based on Purchase History and Location
W.W. Grainger wants to send out a targeted marketing campaign for their new product line. You've been asked to filter their customer database to find all customers who have made a purchase in the last 6 months, live in California or New York, and whose total purchase amount is over $500.
Assuming you have a table that looks like:
Example Input:
| customer_id | first_name | last_name | state |
|---|---|---|---|
| 1001 | John | Smith | CA |
| 1002 | Sara | Davis | NY |
| 1003 | Tim | Brown | TX |
| 1004 | Amy | Johnson | CA |
| 1005 | Steve | Miller | NY |
and a table that looks like:
Example Input:
| purchase_id | customer_id | purchase_date | amount |
|---|---|---|---|
| 3001 | 1001 | 2022-05-01 | $250 |
| 3002 | 1001 | 2022-06-01 | $300 |
| 3003 | 1002 | 2022-02-01 | $200 |
| 3004 | 1003 | 2022-05-01 | $150 |
| 3005 | 1005 | 2022-07-01 | $600 |
Answer:
This query first finds all customers who have made purchases in the last 6 months and whose total purchase amount is over $500 by joining the table with a subquery on the table. It then filters on the customers who live in California or New York by checking the column of the table.
SQL Question 7: In the context of databases, what does atomicity, consistency, isolation, and durability mean?
A DBMS (database management system), in order to ensure transactions are reliable and don't ruin the integrity of the data, tries to mantain the following ACID properties: Atomicity, Consistency, Isolation, and Durability.
To make this concept more concrete, here is what each of the ACID properties would mean in the context of banking transactions:
- Atomicity: a transaction is either completed fully, or not complete at all. For example, if a customer is transferring money from one account to another, the transaction should either transfer the full amount or none at all.
- Consistency: a transaction will only be completed if it follows all database constraints and checks. For example, if a customer is withdrawing money from an account, the transaction should only be completed if the account has sufficient funds available, otherwise the transaction is rejected
- Isolation: ensures that concurrent transactions are isolated from each other, so that the changes made by one transaction cannot be seen by another transaction. This isolation prevents race conditions, like two customers trying to withdraw money from the same account at the same time.
- Durability: ensures that once a transaction has been committed and completed, the changes are permanent. A reset / shutdown of the database shouldn't erase someone's savings accounts!
SQL Question 8: Click-Through-Rate And Conversion Rate Analysis for W.W. Grainger
W.W. Grainger is an American Fortune 500 industrial supply company founded in 1927 in Chicago. The company's promotional activities include digital advertisements exposing users to various products. On their website, users can view the products, add them to their cart, and continue to purchase.
By defining two tables, one for user sessions capturing user interaction with ads; the other for subsequent user actions on the website, we can calculate metrics like click-through-rates (CTR) and conversion rates (CR).
Given the following two tables and , calculate the daily CTR defined as: and daily CR defined as the .
Example Input:
| session_id | user_id | event_date | event_type | product_id |
|---|---|---|---|---|
| 1001 | 780 | 06/25/2022 | ad_view | 9001 |
| 2002 | 192 | 06/25/2022 | ad_click | 9001 |
| 3003 | 192 | 06/26/2022 | ad_view | 6003 |
| 4004 | 980 | 06/27/2022 | ad_click | 6003 |
| 5005 | 192 | 06/28/2022 | ad_view | 9001 |
Example Input:
| action_id | user_id | action_date | action_type | product_id |
|---|---|---|---|---|
| 345 | 780 | 06/25/2022 | add_to_cart | 9001 |
| 345 | 192 | 06/26/2022 | add_to_cart | 6003 |
| 678 | 980 | 06/26/2022 | purchase | 6003 |
| 789 | 192 | 06/28/2022 | add_to_cart | 9001 |
| 901 | 192 | 06/28/2022 | purchase | 9001 |
Answer:
Calculating daily CTR:
Calculating daily CR:
The first query calculates daily click-through-rates by dividing the unique users who clicked an ad by the unique users who viewed an ad on each day. The second query calculates daily conversion rates by dividing the unique users who made a purchase by the unique users who added an item to their cart on each day. Both queries use the function to aggregate actions by day.
To practice another question about calculating rates, solve this TikTok SQL Interview Question within DataLemur's interactive SQL code editor:
W.W. Grainger SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the W.W. Grainger SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the above W.W. Grainger SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like FAANG tech companies and tech startups.
Each DataLemur SQL question has multiple hints, 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 easily right in the browser your SQL query and have it graded.
To prep for the W.W. Grainger SQL interview it is also useful to solve interview questions from other industrial and electrical distribution companies like:
However, if your SQL coding skills are weak, don't worry about going right into solving questions – go learn SQL with this free SQL tutorial.
This tutorial covers topics including aggregate window functions and GROUP BY – both of these come up routinely in W.W. Grainger SQL assessments.
W.W. Grainger Data Science Interview Tips
What Do W.W. Grainger Data Science Interviews Cover?
Beyond writing SQL queries, the other topics to practice for the W.W. Grainger Data Science Interview include:
- Statistics Interview Questions
- Python or R Coding Questions
- Data Case Study Questions
- ML Interview Questions
- Resume-Based Behavioral Questions
How To Prepare for W.W. Grainger Data Science Interviews?
To prepare for the W.W. Grainger Data Science interview make sure you have a deep understanding of the company's cultural values – this will be clutch for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from companies like Google, Tesla, & Goldman Sachs
- A Crash Course on SQL, Product-Sense & ML
- Amazing Reviews (1000+ 5-star reviews on Amazon)