10 Pure Storage SQL Interview Questions (Updated 2024)
At Pure Storage, SQL is used often for analyzing storage performance metrics and as part of their high-performance SQL Server offering. Unsurprisingly this is why Pure Storage almost always asks SQL query questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
So, to help you ace the Pure Storage SQL interview, we've collected 10 Pure Storage SQL interview questions – able to answer them all?
10 Pure Storage SQL Interview Questions
SQL Question 1: Calculate Monthly Average Ratings of Each Product
Given a table named which contains information about user-submitted product reviews, write a SQL query to calculate the average ratings of each Pure Storage product on a monthly basis. Assume all dates are in PST (Pacific Standard Time).
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1 | 101 | 2022-01-01 12:45:00 | 1001 | 5 |
| 2 | 102 | 2022-01-02 15:20:00 | 1002 | 4 |
| 3 | 103 | 2022-01-10 08:30:00 | 1001 | 3 |
| 4 | 104 | 2022-02-01 16:45:00 | 1001 | 4 |
| 5 | 105 | 2022-02-15 09:00:00 | 1002 | 5 |
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 1 | 1001 | 4.00 |
| 1 | 1002 | 4.00 |
| 2 | 1001 | 4.00 |
| 2 | 1002 | 5.00 |
Answer:
This query first extracts the month from the column using the function. Afterwards, the function is used to compute the average rating of each product on a monthly basis. The clause groups the data by month and product, and finally the clause is used to sort the result by month and product.
For more window function practice, solve this Uber SQL Interview Question on DataLemur's interactive coding environment:
SQL Question 2: Average Disk Usage
Given a database of all the storage devices managed by "Pure Storage", create a query that will calculate and provide the average disk usage across all devices in the last month.
Example Input:
| device_id | device_name | total_capacity_gb |
|---|---|---|
| 01 | DeviceA | 1024 |
| 02 | DeviceB | 2048 |
| 03 | DeviceC | 512 |
| 04 | DeviceD | 4096 |
| 05 | DeviceE | 1024 |
Example Input:
| device_id | usage_date | used_capacity_gb |
|---|---|---|
| 01 | 2022-09-15 | 512 |
| 02 | 2022-09-15 | 1024 |
| 03 | 2022-09-15 | 256 |
| 04 | 2022-09-15 | 2048 |
| 05 | 2022-09-15 | 1024 |
| 01 | 2022-08-15 | 256 |
| 02 | 2022-08-15 | 512 |
| 03 | 2022-08-15 | 128 |
| 04 | 2022-08-15 | 1024 |
| 05 | 2022-08-15 | 512 |
Answer:
The subquery in the FROM clause joins the two tables, it calculates the usage ratio for each device in September, and then the outer query calculates the average of these ratios. This provides the average disk usage across all devices managed by "Pure Storage" in the last month.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total usage metric or this Amazon Maximize Prime Item Inventory Question which is similar for dealing with storage capacity.
SQL Question 3: How can you select unique records from a table?
If you want to return records with no duplicates, you can use the keyword in your statement.
For example, if you had a table of Pure Storage employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:
If had the following data:
| f_name | job_title |
|---|---|
| Akash | Data Analyst |
| Brittany | Data Scientist |
| Carlos | Data Engineer |
| Diego | Data Engineer |
| Eva | Data Analyst |
Then the output from the query would be:
| job_title |
|---|
| Data Analyst |
| Data Scientist |
| Data Engineer |
SQL Question 4: Analyzing Click-Through-Rate for Pure Storage
As a Data Analyst at Pure Storage, you are asked to identify the click-through rates (CTRs) of specific digital marketing campaigns. Each time a user sees an ad (an 'impression') and clicks on it for more information, is recorded. The marketing department wants to know the CTR because it helps them understand how engaging their ads are.
To make this analysis more comprehensive, the marketing department is also interested in knowing the conversion rate. A conversion here is defined as a user adding a product to the cart after clicking through the ad.
Here is some sample data structured in two tables: and .
Example Input:
| click_id | user_id | campaign_id | click_time |
|---|---|---|---|
| 101 | 5001 | 2001 | 2021-06-01 14:00:00 |
| 102 | 5002 | 2002 | 2021-06-01 14:05:00 |
| 103 | 5003 | 2001 | 2021-06-01 14:10:00 |
| 104 | 5003 | 2002 | 2021-06-02 15:00:00 |
| 105 | 5002 | 2001 | 2021-06-02 15:05:00 |
Example Input:
| conversion_id | user_id | product_id | add_to_cart_time |
|---|---|---|---|
| 201 | 5001 | 3001 | 2021-06-01 14:10:00 |
| 202 | 5002 | 3002 | 2021-06-01 14:15:00 |
| 203 | 5003 | 3001 | 2021-06-01 14:20:00 |
| 204 | 5003 | 3002 | 2021-06-02 15:10:00 |
| 205 | 5002 | 3001 | 2021-06-02 15:15:00 |
Answer:
This PostgreSQL query provides the Click-Through Rate for each campaign by calculating the ratio of conversions (product added to the cart) to impressions (ad clicks), for clicks that led to a product being added to the cart.
To practice a related SQL problem on DataLemur's free interactive SQL code editor, try this Facebook SQL Interview question:
SQL Question 5: How do the window functions LEAD() and LAG() differ?
Both the and window functions are used to access a row at a specific offset from the current row.
However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.
Often, the offset for both functions is 1, which gives access to the immediately following/preceding row. Here's a SQL query example:
SQL Question 6: Analyzing Sales of Pure Storage Products
Given a table and , where keeps track of all sales transaction data and keeps track of Product details, we want to analyze the revenue generated by each product category. Can you write an SQL query which returns each product category along with the total revenue generated by that category?
Please sort the result in descending order by the total revenue.
Assuming the and tables are structured as follows:
Example Input:
| sale_id | product_id | sale_date | price |
|---|---|---|---|
| 001 | 101 | 02/14/2022 | 800 |
| 002 | 202 | 03/15/2022 | 1500 |
| 003 | 303 | 06/18/2022 | 3000 |
| 004 | 202 | 07/05/2022 | 1500 |
| 005 | 101 | 07/20/2022 | 800 |
Example Input:
| product_id | name | category |
|---|---|---|
| 101 | FlashArray//C | Direct Flash |
| 202 | FlashArray//X | Direct Flash |
| 303 | FlashBlade | Blade Storage |
| 404 | Pure1 | Data Storage |
Answer:
This query joins the and tables based on the and then groups the results by the product category. It calculates the total revenue for each product category by summing up the sale price. The clause is used to sort the results in descending order by .
SQL Question 7: Can you explain what an index is and the various types of indexes?
A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.
There are several types of indexes that can be used in a database:
- Primary index: a unique identifier is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table.
- Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
- Clustered index: determines the physical order of the data in a table
For a concrete example, say you had a table of Pure Storage customer payments with the following columns: payment_id, customer_id, payment_amount, and payment_date.
Here's what a clustered index on the column would look like:
A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values.
Having a clustered index on the column can speed up queries that filter or sort the data based on the payment_date, as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of January, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.
SQL Question 8: Filter Customer Records with LIKE Operator
You are a data analyst at Pure Storage and your manager asked you to execute a task. The company maintains a database of their customers. In this database, , you are given the following fields:
- : A unique identifier for each customer.
- : The full name of the customer.
- : The customer's email address.
- : The product that the customer purchased.
Your task is to filter all the customers who have purchased FlashArrays, and their e-mail addresses end with '@purestorage.com'.
Example Input:
| customer_id | customer_name | email_id | product_purchased |
|---|---|---|---|
| 1 | John | john@purestorage.com | FlashArray//C |
| 2 | Jane | jane@gmail.com | FlashArray//X |
| 3 | Alex | alex@outlook.com | FlashBlade |
| 4 | Maria | maria@purestorage.com | FlashArray//C |
| 5 | Carl | carl@yahoo.com | FlashArray//XL |
Answer:
This query will return all rows where the ends with '@purestorage.com' and the starts with 'FlashArray'. Using the LIKE operator combined with '%' allows us to match any sequence of characters. The '%' before '@purestorage.com' and after 'FlashArray' is a wildcard that matches any sequence of characters.
Example Output:
| customer_id | customer_name | email_id | product_purchased |
|---|---|---|---|
| 1 | John | john@purestorage.com | FlashArray//C |
| 4 | Maria | maria@purestorage.com | FlashArray//C |
SQL Question 9: Calculation of Sales Metrics for Pure Storage Products
Pure Storage, a technology company that provides hardware and software products mainly for data storage, needs an SQL query that calculates the total and average sales for each product every week, as well as the standard deviation for total sales in the past month. Moreover, the company wants to know the percentage change of total sales from one week to the next for each product.
Please consider the following sample data.
Example Input:
| sale_id | sale_date | product_id | unit_price | quantity |
|---|---|---|---|---|
| 1 | 2022-09-01 | 101 | 150 | 5 |
| 2 | 2022-09-03 | 101 | 150 | 3 |
| 3 | 2022-09-10 | 102 | 200 | 2 |
| 4 | 2022-09-10 | 101 | 150 | 2 |
| 5 | 2022-09-17 | 102 | 200 | 4 |
| 6 | 2022-09-17 | 101 | 150 | 7 |
| 7 | 2022-09-24 | 102 | 200 | 3 |
| 8 | 2022-09-24 | 101 | 150 | 3 |
Example Output:
| week_start | product_id | total_sales | avg_weekly_sales | sales_stddev | pct_change |
|---|---|---|---|---|---|
| 2022-08-29 | 101 | 450 | 450 | null | null |
| 2022-09-05 | 101 | 300 | 375 | 106.066017177 | -33.33 |
| 2022-09-12 | 101 | 1050 | 600 | 400 | 250.00 |
| 2022-09-19 | 101 | 450 | 562.50 | 282.842712474 | -57.14 |
| 2022-09-05 | 102 | 400 | 400 | null | null |
| 2022-09-12 | 102 | 800 | 600 | 282.842712474 | 100.00 |
| 2022-09-19 | 102 | 600 | 600 | 141.421356237 | -25.00 |
Answer:
This query first calculates the total sales per week for each product. Then it uses a window function to calculate the average and standard deviation of the total sales over a moving window of the current and the three preceding weeks. Finally, it calculates the percentage change in total sales compared to the preceding week using the function to access the previous row within the window.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating rate of change in sales or this Amazon Highest-Grossing Items Question which is similar for calculating top selling products.
SQL Question 10: Can you give some examples of when denormalization might be a good idea?
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting Pure Storage's database to ever-changing business needs.
How To Prepare for the Pure Storage SQL Interview
The key to acing a Pure Storage SQL interview is to practice, practice, and then practice some more!
Beyond just solving the earlier Pure Storage SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each DataLemur SQL question has hints to guide you, full answers and crucially, there's an interactive coding environment so you can instantly run your query and have it executed.
To prep for the Pure Storage SQL interview it is also useful to solve interview questions from other tech companies like:
But if your SQL foundations are weak, forget about going right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as Subquery vs. CTE and CASE/WHEN statements – both of these show up frequently during Pure Storage SQL interviews.
Pure Storage Data Science Interview Tips
What Do Pure Storage Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories to prepare for the Pure Storage Data Science Interview are:
- Probability & Statistics Questions
- Python Pandas or R Coding Questions
- Business Sense and Product-Sense Questions
- ML Interview Questions
- Behavioral Interview Questions
How To Prepare for Pure Storage Data Science Interviews?
To prepare for Pure Storage Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from companies like Google, Tesla, & Goldman Sachs
- a refresher covering Product Analytics, SQL & ML
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