Data Science, Data Engineering, and Data Analytics employees at Veritiv use SQL to analyze logistical data, which helps them improve supply chain processes and make delivery operations more efficient. They also use it to manage large customer databases to enhance their market strategies and tailor their services to better align with customer preferences, this is why Veritiv asks SQL problems during interviews.
So, to help you prep for the Veritiv SQL interview, here's 9 Veritiv SQL interview questions in this article.
Veritiv is a Fortune 500 company that sells packaging, facility solutions, print and publishing machinery and services. As a data analyst interviewing at Veritiv, one of your tasks might involve identifying top customers (power users) based on certain criteria. In this case, we're interested in customers who place orders frequently and whose order values are significantly high. For this question, write a SQL query that can find the top 10 users who have made the most number of orders and whose total order value is the highest.
Consider the following tables:
customer_id | first_name | last_name | |
---|---|---|---|
1001 | John | Doe | john.doe@gmail.com |
1002 | Jane | Smith | jane.smith@gmail.com |
1003 | Peter | Parker | peter.parker@gmail.com |
order_id | customer_id | order_date | order_value |
---|---|---|---|
9001 | 1001 | 06/08/2022 | 500 |
9002 | 1001 | 06/10/2022 | 600 |
9003 | 1002 | 06/15/2022 | 200 |
9004 | 1002 | 06/20/2022 | 150 |
9005 | 1003 | 06/22/2022 | 400 |
The above query first joins the and tables on the field. Then it groups the result by and calculates the total order count () and the sum of order value () for each customer. Finally, it orders the result in descending order by and then by , and limits the output to top 10 customers. This will give us a list of top 10 power users based on order frequency and value.
To work on a similar customer analytics SQL question where you can solve it interactively and have your SQL code instantly graded, try this Walmart SQL Interview Question:
Assume there was a table of Veritiv employee salary data. Write a SQL query to find the top 3 highest earning employees within each department.
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 |
department_id | department_name |
---|---|
1 | Data Analytics |
2 | Data Science |
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 |
Check your SQL query for this problem and run your code right in DataLemur's online SQL environment:
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 hard to understand, you can find a detailed solution here: Top 3 Department Salaries.
Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.
Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.
Unique Indexes are blazing fast. Non unique indexes can improve query performance, but they are often slower because of their non-unique nature.
Suppose you are a data analyst at Veritiv and you are given a table named that includes data for each sale made by the company. The table has the columns: , , , and .
Your task is to write a SQL query to calculate the running total of sales for each product by the order of sale date. The running total for a sale of a product is the sum of the for that product up to and including that sale.
The output of the query should contain the following columns: , and .
Here are sample tables:
sale_id | product_id | sale_date | sale_amount |
---|---|---|---|
1 | 101 | 2022-01-01 | 20 |
2 | 102 | 2022-01-03 | 30 |
3 | 101 | 2022-01-04 | 40 |
4 | 103 | 2022-01-04 | 20 |
5 | 101 | 2022-01-06 | 10 |
product_id | sale_date | running_total |
---|---|---|
101 | 2022-01-01 | 20 |
101 | 2022-01-04 | 60 |
101 | 2022-01-06 | 70 |
102 | 2022-01-03 | 30 |
103 | 2022-01-04 | 20 |
Here is a PostgreSQL query to produce the requested running total of sales for each product:
This query uses the SQL window function with the clause to calculate the running total of for each . The in the clause divides the table into partitions by , and the clause sorts each partition by . The phrase tells PostgreSQL to calculate the sum of from the first row of each partition to the current row. Finally, the query sorts the result by and .
p.s. Window functions show up pretty frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.
For a concrete example, suppose you were a Data Scientist on the Marketing Analytics team at Veritiv. A task you would encounter freqently would be to calculate the conversion rate for Veritiv's ads over a certain time period, for which you would write the following stored procedure:
To use this conversion rate stored procedure, you could call it like this:
Veritiv is a North American leader in business-to-business distribution solutions. As part of their business, they need to keep track of their inventory across various warehouses. Given this scenario, design a database that will enable them to store and track information about their products, warehouses, and stock levels.
Additionally, can you write a PostgreSQL query to retrieve a list of products, which stock level is below a specified limit across all warehouses?
Sample tables:
product_id | product_name | unit_price |
---|---|---|
1001 | Paper A4 | 10.00 |
1002 | Ink Black | 15.00 |
1003 | Stapler | 20.00 |
warehouse_id | warehouse_location |
---|---|
2001 | Atlanta |
2002 | New York |
2003 | Los Angeles |
product_id | warehouse_id | stock_level |
---|---|---|
1001 | 2001 | 1000 |
1001 | 2002 | 500 |
1001 | 2003 | 1500 |
1002 | 2001 | 800 |
1002 | 2002 | 500 |
1002 | 2003 | 700 |
1003 | 2001 | 300 |
1003 | 2002 | 250 |
1003 | 2003 | 200 |
This query will return a list of products along with their location and stock level where the stock level is below 500. The query first joins the , , and tables using their keys, and then applies a filter to return only rows where the stock level is less than 500.
While both types of databases are used to store data (no duh!), relational databases and non-relational (also known as NoSQL databases) differ in a few important ways:
Data model: Relational databases use a data model consisting of tables and rows, while NoSQL databases use a variety of data models, including document, key-value, columnar, and graph storage formats.
Data integrity: Relational databases use structured query language (SQL) and enforce strict data integrity rules through the use of foreign keys and transactions. NoSQL databases may not use SQL and may have more relaxed data integrity rules.
Structure: Relational databases store data in a fixed, structured format, while NoSQL databases allow for more flexibility in terms of data structure.
ACID compliance: Relational databases are typically into shrooms and are ACID-compliant (atomic, consistent, isolated, and durable), while NoSQL databases may not be fully ACID-compliant (but they try their best... and it's effort that counts...or at least that's what my gym teacher told me!)
For this question, we will assume you are a data analyst at Veritiv. Veritiv is running a digital marketing campaign with a series of ads and wants to better understand how these ads are performing.
The focus highlights are:
The CTR is calculated as (Number of Clicks / Number of Impressions)*100.
The Conversion Rate is calculated as (Number of users added the product into the cart after viewing / Number of users viewed the product)*100.
The company has the following tables available:
ad_id | impressions | clicks |
---|---|---|
1001 | 5000 | 300 |
1002 | 6500 | 400 |
1003 | 7800 | 550 |
1004 | 8500 | 600 |
1005 | 7100 | 500 |
product_id | viewed | added_to_cart |
---|---|---|
P101 | 4500 | 800 |
P102 | 6800 | 900 |
P103 | 7900 | 1100 |
P104 | 8600 | 1250 |
P105 | 7200 | 1050 |
Write a PostgreSQL query to calculate the Click-Through-Rate for each ad and the Conversion Rate for each product.
This query calculates the Click-Through-Rate (CTR) for each ad in the table by dividing the number of clicks by the impressions for each ad. It also calculates the Conversion Rate for each product in the table by dividing the number of times the product was added to the cart (after being viewed) by the number of views for each product. The result will provide a list of ads with their respective CTR and a list of products with their respective Conversion Rates.
To practice a related problem on DataLemur's free online SQL code editor, attempt this Facebook SQL Interview question:
Veritiv Corporation is a North American leader in business-to-business distribution solutions. They help shape their customers' success through a pioneering approach to logistics, product selection and services.
You work in the data department and have been provided with two tables:
Your task is to write an SQL query to find the average price of each type of product sold by the company.
product_id | product_name | product_type |
---|---|---|
1001 | Product A | Paper |
1002 | Product B | Packaging |
1003 | Product C | Packaging |
1004 | Product D | Facility |
sale_id | product_id | sale_price |
---|---|---|
2001 | 1001 | 15.00 |
2002 | 1001 | 14.50 |
2003 | 1002 | 20.00 |
2004 | 1003 | 22.50 |
2005 | 1004 | 25.00 |
product_type | avg_price |
---|---|
Paper | 14.75 |
Packaging | 21.25 |
Facility | 25.00 |
This SQL query joins the and tables on the field. It then groups the data by , and for each , it calculates the average . The resulting table will have two columns: and , with the latter representing the average sale price for each type of product.
The key to acing a Veritiv SQL interview is to practice, practice, and then practice some more! In addition to solving the earlier Veritiv SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Google, Uber, and Microsoft.
Each DataLemur SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there is an online SQL code editor so you can easily right in the browser your SQL query answer and have it checked.
To prep for the Veritiv SQL interview you can also be a great idea to practice SQL problems from other industrial and electrical distribution companies like:
In case your SQL foundations are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this free SQL tutorial.
This tutorial covers topics including using ORDER BY and Subquery vs. CTE – both of these pop up frequently in SQL job interviews at Veritiv.
In addition to SQL interview questions, the other types of problems to prepare for the Veritiv Data Science Interview are:
I think the best way to prep for Veritiv Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
It solves 201 data interview questions sourced from Facebook, Google, & Amazon. The book's also got a crash course on Python, SQL & ML. And finally it's vouched for by the data community, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.
While the book is more technical, it's also crucial to prepare for the Veritiv behavioral interview. A good place to start is by reading the company's unique cultural values.