9 Digital Realty SQL Interview Questions (Updated 2024)
Data Scientists, Analysts, and Data Engineers at Digital Realty write SQL queries almost every single workday. They use SQL for analyzing large datasets to improve infrastructure management, and managing tenant data for enhanced customer service. That's why Digital Realty asks prospective hires SQL interview problems.
So, to help you prep for the Digital Realty SQL interview, here’s 9 Digital Realty Trust SQL interview questions – scroll down to start solving them!
9 Digital Realty Trust SQL Interview Questions
SQL Question 1: Identifying Power Users at Digital Realty
Digital Realty wants to identify power users from their dataset. Power users are those who have leased maximum data center space in terms of square feet over the past year. The company uses two tables for their data; a table that contains user information, and a table that contains information about each lease, including the user that took the lease and the square footage of the lease.
Use these details to write a query that identifies the top 5 users who have leased the most square footage in the past year.
Example Input:
| user_id | user_name |
|---|---|
| 001 | Alpha Co |
| 002 | Beta Corp |
| 003 | Gamma LTD |
| 004 | Delta Inc |
Example Input:
| lease_id | user_id | lease_date | lease_sq_ft |
|---|---|---|---|
| 9001 | 002 | 01/15/2021 | 4000 |
| 9002 | 003 | 03/20/2021 | 5500 |
| 9003 | 001 | 04/30/2021 | 3500 |
| 9004 | 004 | 06/10/2021 | 6000 |
| 9005 | 001 | 08/25/2021 | 2000 |
| 9006 | 003 | 10/20/2021 | 6000 |
| 9007 | 002 | 12/10/2021 | 4500 |
| 9008 | 004 | 02/15/2022 | 3000 |
Answer:
This query joins the Users and Leases tables on the user_id field, then filters for leases in the past year. It sums the lease_sq_ft field for each user, and orders the results by the total square footage leased in descending order. It finally limits the results to the top 5 users. This provides the 5 'power users' who leased the most space in the past year.
To work on another SQL customer analytics question where you can solve it interactively and have your SQL query automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Department Salaries
You're given a table of Digital Realty employee and department salary information. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Code your solution to this problem interactively on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution here: Department vs. Company Salary.
Learn about how Digital Reality uses partner solutions like AWS and Oracle Cloud.
SQL Question 3: What is database denormalization, and when is it a good idea to consider it?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
A few reasons to denormalize a database:
-
Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases at Digital Realty, as joins can be expensive and slow.
-
Scalability: Denormalization can be like a turbo boost for your database's scalability! By requiring less data to be read and processed when executing a query, denormalization can help your database handle a ton of queries without breaking a sweat.
-
Simplification: One way to simplify the design of a database is by using denormalization to reduce the number of tables and relationships that need to be managed. This can make the database easier to understand and maintain.
Of course, don't over-do the database denormalization magic – too much redundancy can increase data storage costs, and get complicated to manage if there's frequent commands that need to be run.
SQL Question 4: Data Center Traffic Analysis
As a data analyst at Digital Realty, you have been given access to a database that records the network traffic of your company's data centers. Your task is to write a SQL query to analyze the total and average bandwidth usage over time. More specifically, you are required to calculate the total in traffic, out traffic and average in and out traffic for each data center by month and server rack. Use PostgreSQL window functions to accomplish this.
Example Input:
| record_id | datacenter_id | server_rack_id | date_time | in_traffic_gb | out_traffic_gb |
|---|---|---|---|---|---|
| 1 | 1 | A1 | 01/02/2022 13:14:00 | 20 | 18 |
| 2 | 1 | A1 | 01/15/2022 11:03:00 | 22 | 21 |
| 3 | 1 | B2 | 01/06/2022 05:20:00 | 18 | 20 |
| 4 | 1 | B2 | 01/28/2022 19:49:00 | 23 | 24 |
| 5 | 2 | A1 | 01/10/2022 09:11:00 | 28 | 26 |
| 6 | 2 | A1 | 01/22/2022 07:16:00 | 31 | 33 |
| 7 | 2 | B2 | 01/03/2022 03:12:00 | 18 | 19 |
| 8 | 2 | B2 | 01/30/2022 22:55:00 | 27 | 28 |
Example Output:
| month | datacenter | server_rack | total_in_traffic_gb | avg_in_traffic_gb | total_out_traffic_gb | avg_out_traffic_gb |
|---|---|---|---|---|---|---|
| 1 | 1 | A1 | 42 | 21.00 | 39 | 19.50 |
| 1 | 1 | B2 | 41 | 20.50 | 44 | 22.00 |
| 1 | 2 | A1 | 59 | 29.50 | 59 | 29.50 |
| 1 | 2 | B2 | 45 | 22.50 | 47 | 23.50 |
Answer:
In this SQL statement, we used the PostgreSQL window functions and to calculate the total and average in and out traffic for each data center by month and server rack. The clause is used to specify the groups (i.e., data center, month, and server rack) over which the window function is to be computed. The clause is used to order the result by month, data center, and server rack.
For more window function practice, try this Uber SQL problem on DataLemur's online SQL code editor:
SQL Question 5: Can you explain what SQL constraints are, and why they are useful?
SQL constraints are used to specify rules for the data in a table. They can be applied to single or multiple fields in a table when the table is created, or after the table has been created using the ALTER TABLE command.
For example, say you had a database that stores ad campaign data from Digital Realty's Google Analytics account.
Here's what some constraints could look like:
SQL Question 6: Average Monthly Revenue per Data Centre
Digital Realty is a real estate investment trust that invests in carrier-neutral data centers and provides colocation and peering services. As an SQL developer, we want you to provide the average monthly revenue generated by each data centre.
The 'data_centre' table has the following columns: 'centre_id' (Unique Identifier for the data centre), 'name' (Name of the data centre), 'city' (City where the data centre is located), 'country' (The country where the data centre is located).
The 'customers' table has the following columns: 'customer_id' (Uniquely Identifies a customer), 'name' (Name of the customer), 'email' (Email of the customer).
The 'billing' table has the following columns: 'billing_id' (Uniquely Identifies a billing event) , 'centre_id' (The centre that is billing), 'customer_id' (The customer receiving the bill), 'bill_date' (Date of the bill), 'amount' (The amount of the bill).
Example Input:
| centre_id | name | city | country |
|---|---|---|---|
| 1 | Centre A | New York | USA |
| 2 | Centre B | London | UK |
| 3 | Centre C | Singapore | Singapore |
Example Input:
| customer_id | name | |
|---|---|---|
| 101 | Company A | companyA@example.com |
| 102 | Company B | companyB@example.com |
| 103 | Company C | companyC@example.com |
Example Input:
| billing_id | centre_id | customer_id | bill_date | amount |
|---|---|---|---|---|
| 10001 | 1 | 101 | 2022-01-01 | 20000 |
| 10002 | 2 | 102 | 2022-01-02 | 30000 |
| 10003 | 1 | 102 | 2022-02-01 | 25000 |
| 10004 | 3 | 103 | 2022-01-03 | 15000 |
| 10005 | 3 | 103 | 2022-02-02 | 20000 |
Answer:
This query first joins the 'billing' table with the 'data_centre' table on 'centre_id'. Then it calculates the average revenue generated by each data centre per month by grouping the records based on the month of 'bill_date' and the 'name' of the data centre. It finally sorts the result in ascending order of month and descending order of the average revenue.
SQL Question 7: Can you explain the purpose of the constraint and give an example of when you might use it?
The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.The CHECK constraint is often used in conjunction with other constraints, such as NOT NULL or UNIQUE.
You might consider implementing the CHECK constraint in your database if you want to ensure that certain data meets specific conditions. This can be helpful for maintaining the quality and reliability of your data.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
SQL Question 8: Filter Client List for "Digital Realty" in City Name
In a company database, there is a table that records client details. The management team at Digital Realty wants to understand their service spread and they need a list of all their clients who have 'Realty' in their city name. You are to write an SQL query to filter out these records.
Sample Input:
| client_id | client_name | city_name |
|---|---|---|
| 1251 | Client A | Digital Realty |
| 6891 | Client B | Realty Digital |
| 8956 | Client C | New York |
| 6203 | Client D | Los Angeles Realty |
| 7284 | Client E | Digital Realty |
Example Output:
| client_id | client_name | city_name |
|---|---|---|
| 1251 | Client A | Digital Realty |
| 6891 | Client B | Realty Digital |
| 6203 | Client D | Los Angeles Realty |
| 7284 | Client E | Digital Realty |
Answer:
The above query will filter all records where 'Realty' is found in the field. The LIKE keyword combined with a wildcard character (%) in SQL is used to search for a specified pattern in a column. Here, '%Realty%' means any string that has 'Realty' in any position. As a result, we get all clients who have 'Realty' in their city names.
SQL Question 9: Analyze Customer and Service Usage Data
As a data analyst at Digital Realty, your task is to understand customer usage of services provided. Two key databases you have are and . Assume the table holds data of customer information and the table stores usage information of services used by these customers.
Your task is to write a SQL query to get the total usage of services for each customer. Include customers' full names and their email addresses from the table and total service usage from the table.
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 56789 | John | Doe | john.doe@digi.com |
| 98765 | Jane | Smith | jane.smith@digi.com |
| 45678 | Alice | Johnson | alice.johnson@digi.com |
| 87654 | Bob | Williams | bob.williams@digi.com |
Example Input:
| usage_id | customer_id | service_id | usage |
|---|---|---|---|
| 1001 | 56789 | 201 | 50 |
| 1002 | 98765 | 202 | 30 |
| 1003 | 45678 | 203 | 70 |
| 1004 | 87654 | 204 | 80 |
| 1005 | 56789 | 205 | 50 |
Answer:
We can use JOIN to combine the information from both tables. Since not all customers have used services, a LEFT JOIN is used so all customers are included in the output. This can be done using the following SQL query in PostgreSQL:
This query first joins the table (alias as c) and the table (alias as s) on . It groups the result by , and for each customer, it calculates the by summing the from table. If a customer has no usage record, COALESCE is used to return 0 instead.
Because join questions come up frequently during SQL interviews, take a stab at an interactive SQL join question from Spotify:
How To Prepare for the Digital Realty SQL Interview
The key to acing a Digital Realty SQL interview is to practice, practice, and then practice some more!
Beyond just solving the above Digital Realty SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each problem on DataLemur has multiple hints, full answers and crucially, there is an interactive coding environment so you can right online code up your SQL query and have it graded.
To prep for the Digital Realty SQL interview it is also wise to solve SQL questions from other datacenter and REIT companies like:
However, if your SQL coding skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.
This tutorial covers SQL concepts such as inner vs. outer JOIN and handling strings – both of these pop up frequently in Digital Realty SQL assessments.
Digital Realty Trust Data Science Interview Tips
What Do Digital Realty Data Science Interviews Cover?
In addition to SQL interview questions, the other types of questions covered in the Digital Realty Data Science Interview are:
- Statistics and Probability Questions
- Python or R Programming Questions
- Product-Sense Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Digital Realty Data Science Interviews?
I'm sort of biased, but I think the best way to prep for Digital Realty Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
The book has 201 interview questions sourced from FAANG & startups. It also has a refresher covering SQL, Product-Sense & ML. And finally it's helped a TON of people, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.