8 Seagate SQL Interview Questions (Updated 2024)
At Seagate Technology, SQL does the heavy lifting for analyzing manufacturing data for quality control and predicting machinery maintenance needs using historical performance data. Because of this, Seagate almost always evaluates jobseekers on SQL problems during interviews for Data Analyst, Data Science, and BI jobs.
So, to help you ace the Seagate SQL interview, this blog covers 8 Seagate Technology SQL interview questions – able to answer them all?
8 Seagate Technology SQL Interview Questions
SQL Question 1: Identify Top Customers with Most Purchases
Seagate, being a data storage companies, has a diverse customer-base. To support their customer retention efforts, Seagate needs to identify the customers who have made the most purchases. They look at not only the amount of products bought but also the frequency of purchases. Write a SQL query to identify the top 10 customers who made the most orders in the last year.
Example Input:
| customer_id | name |
|---|---|
| 1 | John Doe |
| 2 | Jane Doe |
| 3 | Mark Twain |
| 4 | Emily Dickinson |
| 5 | Ernest Hemingway |
Example Input:
| order_id | customer_id | order_date | product_id | quantity |
|---|---|---|---|---|
| 1 | 1 | 01/02/2022 | 1001 | 2 |
| 2 | 2 | 01/02/2022 | 1002 | 3 |
| 3 | 1 | 01/03/2022 | 1003 | 1 |
| 4 | 2 | 01/03/2022 | 1001 | 2 |
| 5 | 2 | 01/04/2022 | 1002 | 1 |
| 6 | 3 | 01/05/2022 | 1004 | 4 |
| 7 | 4 | 01/05/2022 | 1001 | 3 |
| 8 | 4 | 01/06/2022 | 1003 | 2 |
| 9 | 5 | 01/06/2022 | 1004 | 2 |
| 10 | 5 | 01/07/2022 | 1002 | 1 |
Answer:
In this query, we are joining the table with the table on the field. The WHERE clause filters the orders to include only those made in the last year. The query then groups the result set by customer name and counts the number of orders made by each customer. Finally, the results are ordered in descending order by the count of orders, and limited to the top ten.
To practice a similar customer analytics SQL question where you can solve it right in the browser and have your SQL query automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Seagate Product Monthly Average Rating
Seagate is interested in understanding the average rating their products receive each month. Based on the following table, please write a SQL query that calculates the average product rating per month.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1 | 123 | 2022-06-08 | 50001 | 4 |
| 2 | 265 | 2022-06-10 | 69852 | 4 |
| 3 | 362 | 2022-06-18 | 50001 | 3 |
| 4 | 192 | 2022-07-26 | 69852 | 3 |
| 5 | 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:
Here is the PostgreSQL query that you can use:
This query uses the function to fetch the month from . It then forms groups comprising of each unique month and product id combination, and calculates the average stars received for each group. We order the result by month and product id to better visualize the data.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 3: How does the RANK() window function differ from DENSE_RANK()?
As the name implies, the window function ranks each row within your window/partition. If two rows have the same rank, the next number in the ranking will be the previous rank plus the number of duplicates. For example, if you've got three records tied for 5th place, they'll get the values 5, 6, and 7, and the next row after this 3-way tie will have a rank of 8.
The function assigns a distinct rank to each row within a partition based on the provided column value with no gaps. This function will assign the same rank to two rows if they have the same rank, and give the next row the next rank number. To make this more concrete, imagine you had three records at rank 5 – then, the next rank would be 6.
SQL Question 4: Filter Customers Based on Product Purchases and Locations
Given a database of customer records, write an SQL query to filter out all customers who have purchased a specified set of Seagate products and live in a given geographic area. The database is divided into two tables; the 'customers' table with customer details including their geographical locations, and the 'purchases' table storing the various products bought by these customers.
Example Input:
| customer_id | first_name | last_name | city | state | country |
|---|---|---|---|---|---|
| 001 | James | Smith | Los Angeles | CA | USA |
| 002 | Helen | Brown | New York | NY | USA |
| 003 | Paul | Davis | Dallas | TX | USA |
| 004 | Olivia | Johnson | Chicago | IL | USA |
| 005 | John | Miller | San Francisco | CA | USA |
Example Input:
| customer_id | product_id | product_name |
|---|---|---|
| 001 | PRD01 | Seagate Hard Disk |
| 002 | PRD02 | Seagate SSD |
| 003 | PRD01 | Seagate Hard Disk |
| 004 | PRD02 | Seagate SSD |
| 005 | PRD03 | Seagate Portable Drive |
Answer:
Here's an SQL query that filters out the customers who have bought either the "Seagate Hard Disk" or "Seagate SSD" products and reside in "CA", USA.
The query joins the 'customers' and 'purchases' tables on the 'customer_id' field, then filters by the state (in this case, 'CA') and the product name (either 'Seagate Hard Disk' or 'Seagate SSD'). It returns the customer details for those who satisfy both conditions.
SQL Question 5: What does the SQL function do?
The COALESCE() function can be used to replace NULL values with a specified value. For instance, if you are a Data Analyst at a company and are working on a customer analytics project to identify the most engaged customers, you may have access to the following data in the table:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | very_active |
| 601 | NULL | NULL |
Before you could procede with your analysis, you would need to remove the NULLs, and replace them with the default value for email engagement (not_active), and the default sms_engagement level (not_opted_in).
To do this, you'd run the following query:
This would get you the following output:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 102 | un-subscribed | not_opted_in |
| 103 | not_active | not_opted_in |
| 104 | not_active | very_active |
| 105 | very_active | very_active |
| 106 | not_active | not_opted_in |
SQL Question 6: Calculate the Average Storage Size of All Seagate Hard Disk Sold
For a technology company like Seagate, your job could involve analyzing the amount of storage size in their Hard Disk drives. Due to the nature of their products, one question you could be asked is to find the average storage sizes of all the Hard Drives sold by them. This would be a good test of your ability to use the AVG function.
Example Input:
| product_id | model | size_in_gb |
|---|---|---|
| 1 | 'Barracuda' | 500 |
| 2 | 'Firecuda' | 1000 |
| 3 | 'Skyhawk' | 2000 |
| 4 | 'IronWolf' | 4000 |
| 5 | 'Exos X' | 8000 |
Example Input:
| sale_id | product_id | quantity_sold |
|---|---|---|
| 101 | 1 | 200 |
| 102 | 2 | 300 |
| 103 | 3 | 150 |
| 104 | 4 | 175 |
| 105 | 5 | 250 |
Answer:
This query begins by joining the table with the table, using the column that is common to both. It then selects the average () size of the hard drives (), calculating it based on all rows in the joined table.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for finding highest-grossing items in categories or this Amazon Average Review Ratings Question which is similar for calculating average values for each product.
SQL Question 7: What's the difference between relational and NoSQL databases?
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, most importantly on the way data is stored. 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.
This added flexibilty makes NoSQL databases great for non-tabular data (like hierarchal data or JSON data), or data where the type/format is constantly evolving. With this added flexibility, comes one big weakness – you won't get ACID-compliance. That means, unlike relational databases which are typically adhere to the ACID properties (atomic, consistent, isolated, and durable), you don't get as strong guarentees with most non-relational databases.
SQL Question 8: Calculation of discount and final price of hard drives
Imagine you are working for Seagate, a leading storage solution company. The sales department often announces discounts on their products. The discounts are offered in terms of percentage. The discounts are not always integer values but can also be decimal numbers. Given a table with product id, name, and original prices, write a SQL Query to calculate the discount amount and the final price after discount. Also, Seagate wants you to round off the final price to the nearest integer.
Example Input:
| product_id | name | price | discount_percentage |
|---|---|---|---|
| 1 | Hard Drive A | 5000 | 15.5 |
| 2 | Hard Drive B | 3000 | 10 |
| 3 | Hard Drive C | 7000 | 4.1 |
| 4 | Hard Drive D | 9000 | 0 |
| 5 | Hard Drive E | 4500 | 30 |
Answer:
In PostgreSql we use the ROUND() function to round off values and the ABS() function to ensure that there's no negative price which doesn't make sense.
This query calculates the discount amount by substracting the discounted price (calculated with ) from the original price of the product. The final price is calculated by subtracting the discount percentage from 1 and multiplying it by the original price. To ensure that we don't have negative prices, the absolute value function ABS() is used. The final price is rounded to the nearest integer using the ROUND() function.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring calculations based on product prices, or this McKinsey 3-Topping Pizzas Question which is similar for involving price calculations with a given item list.
Preparing For The Seagate SQL Interview
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews.
Besides solving the earlier Seagate SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each exercise has multiple hints, full answers and best of all, there is an online SQL code editor so you can right in the browser run your query and have it graded.
To prep for the Seagate SQL interview it is also helpful to solve SQL questions from other tech companies like:
However, if your SQL query skills are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
This tutorial covers SQL topics like RANK() window functions and LEAD/LAG – both of these come up frequently in Seagate SQL assessments.
Seagate Technology Data Science Interview Tips
What Do Seagate Data Science Interviews Cover?
In addition to SQL interview questions, the other types of problems tested in the Seagate Data Science Interview are:
- Statistics Interview Questions
- Python Pandas or R Coding Questions
- Data Case Study Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral Based Interview Questions
How To Prepare for Seagate Data Science Interviews?
To prepare for Seagate Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG, tech startups, and Wall Street
- a crash course on Python, SQL & ML
- over 900+ reviews on Amazon & 4.5-star rating