10 Datadog SQL Interview Questions (Updated 2024)

At Datadog, SQL does the heavy lifting for analyzing large datasets to understand customer behavior and optimizing their cloud-based, monitoring service for improved server performance. Unsurprisingly this is why Datadog almost always evaluates jobseekers on SQL questions in interviews for Data Science, Analytics, and & Data Engineering jobs.

In case you're stressed about an upcoming SQL Interview, here’s 10 Datadog SQL interview questions to practice, which are similar to recently asked questions at Datadog – can you solve them?

10 Datadog SQL Interview Questions

SQL Question 1: Average Performance Score by Server and Hour

Let's imagine you are a Data Analyst at Datadog, a monitoring service for cloud-scale applications. Datadog has a metric called which measures the performance of different servers.

Each server has an associated . The performance score of each server is recorded every hour, and the timestamp of the measure is in field. All these details are recorded in a table called .

The task is to write a SQL query that can identify trends in server performance. Specifically, calculate the average of each server () for each hour of the day, using window functions.

Here's a markdown representation of the table to use for this query:

Example Input:

Answer:

In the above SQL query, we first extract the hour from the column. We then calculate the average for each and using the AVG() window function. The PARTITION BY clause is used to split the data into different groups for the calculation of the average . The ORDER BY clause is used to ensure we get the results in a structured manner.

To practice a similar SQL problem on DataLemur's free online SQL coding environment, solve this SQL interview question asked by Facebook:

SQL Question 2: Filtering Customer's Data

Datadog records customer interactions in two tables: for general customer information, and for touchpoints with each customer (e.g., technical support, sales calls etc.). Write a PostgreSQL query to retrieve a list of all customers (customer_id and customer_name) based in 'USA' who have had more than ten interactions in the last six months.

Example Input:

Example Input:

Answer:

This query first generates a subquery, , that groups all interactions in the last six months by and filters only those that have more than ten interactions. Then, we join this subquery with the table on to retrieve the customer's names. The final filtering condition, , only keeps customers based in the USA.

To practice a similar SQL problem on DataLemur's free interactive coding environment, solve this Facebook SQL Interview question:

SQL Question 3: Why are foreign key's important in databases?

A foreign key is a field in a database table that serves as a reference to the primary key of another table, allowing for the creation of a relationship between the two tables.

For a concrete example, let's inspect employee data from Datadog's HR database:

:

+------------+------------+------------+------------+ | employee_id| first_name | last_name | manager_id | +------------+------------+------------+------------+ | 1 | Aubrey | Graham | 3 | | 2 | Marshal | Mathers | 3 | | 3 | Dwayne | Carter | 4 | | 4 | Shawn | Carter | | +------------+------------+------------+------------+

In this table, is the primary key, and is used to uniquely identify each row.

could be a foreign key. It references the of the manager of each employee in the table, establishing a relationship between the employees and their managers. This foreign key allows you to easily query the table to find out who a specific employee's manager is, or to find out which employees report to a specific manager.

It is possible for a table to have multiple foreign keys that reference primary keys in different tables. For instance, the table could have additional foreign keys for the of the department where an employee works and the of the employee's location.

SQL Question 4: Computing Click-through and Conversion Rates for Datadog's Digital Products

Datadog has a range of digital products which they continually market through various channels. Given the tables that records every time a user clicks on one of Datadog's ads, and that records every time a user goes from viewing a product to adding it into their cart, write a SQL query that would be able to calculate the click-through rate (CTR) and the conversion rate (Conversion Rate) for the source 'Facebook' in December 2022.

Example Input:

Example Input:

Answer:

This query firstly joins the and tables on and . This has the effect of connecting every click event with its corresponding conversion event, if it exists. The WHERE clause then filters the source to be 'Facebook' and the month and year to be December 2022. Then, the conversion rate is calculated as the ratio of total conversions to total clicks for the source 'Facebook'. The result will be a row for each product_id, which will display the source name, total clicks, total conversions, and the conversion rate for December 2022.

To solve a similar SQL problem on DataLemur's free online SQL coding environment, try this Meta SQL interview question:

SQL Question 5: What's the purpose of the function in SQL?

The function is used to remove NULLs, and replace them with another value.

For example, say you were a Data Analyst at Datadog and were doing a customer analytics project to see who the most marketing-engaged customers were, and had access to the below table.

datadog_customers:

Before you could procede, you had to remove the NULLs, and replace them with the default value (not_active for email, and not_opted_in for SMS). To do this, you'd run the following query:

This would get you the following output:

SQL Question 6: Average response time per service

Datadog is a monitoring and analytics platform for developers, IT operations teams, and business users. Let's take the example where we need to examine the average response times of the APIs for different services sampled at various timestamps.

In this case, the tables will have the following structure:

Example Input:

You are required to find the average response time of each service in the 'api_logs' table.

Example Output:

Answer:

This SQL query groups the records by service_name and calculates the average response_time for each group.

The output will show each service_name and their corresponding average response_time in milliseconds. This can be used to provide insights into the performance of different services in the platform.

To practice a similar problem on DataLemur's free online SQL code editor, try this SQL interview question asked by Facebook:

SQL Question 7: Can you give an example of a one-to-one relationship between two entities, vs. a one-to-many relationship?

In database schema design, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a US citizen and their social-security number (SSN) - each citizen has one SSN, and each SSN belongs to one person.

On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. A teacher's relationship with their classes is an example of this - a teacher can teach many classes, but each class is only associated with one teacher.

SQL Question 8: Find Client Information with Specific Patterns

In the Datadog's customer records database, it is crucial to find certain patterns in the client's email domain to understand the type and spread of clients. Write a SQL query that will allow us to retrieve all records of clients whose email domain is 'gmail.com'.

Assume that we have the following table:

Example Input:

Your task is to pull out the client_id, first_name, last_name and email of clients using 'gmail.com' as their email domain.

Answer:

The SQL query uses the LIKE operator to find all clients whose emails end with 'gmail.com'. The '%' symbol is a wildcard character that matches any sequence of characters. In this context, this will match any string that ends with 'gmail.com'.

To practice a similar problem about calculating rates, solve this TikTok SQL Interview Question on DataLemur's online SQL code editor:

SQL Question 9: Calculating Rajon's Performance Metrics

Rajon is a service engineer at Datadog, and he works in shifts. He keeps a record of his work periods and the number of errors he finds during his work sessions. We want to calculate Rajon’s work efficiency in terms of errors found per hour.

For this, we have a table where each row indicates a shift Rajon has worked, the start and end times of his shifts (in the format ), and the number of errors found during that shift.

Example Input:

Now, let's write a PostgreSQL query to calculate the working hours for each shift (considering the fact that some shifts span two days), calculate the number of errors found per hour, and round the result to two decimal places.

Answer:

This query works by calculating the difference between the end time and start time of each shift in hours, considering the possibility of a shift's end time being smaller than the start time (which means the shift spans two days). It then divides the number of errors found by these hours worked and rounds the resulting errors per hour to two decimal places. The function is used to convert the interval to seconds, which we then convert to hours by dividing by 3600. The statement is used to handle shifts that span two days.

To solve a related problem on DataLemur's free interactive SQL code editor, try this Meta SQL interview question:

SQL Question 10: What's the difference between a unique index and non-unique index?

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.

To define a unique index in PostgreSQL, you can use the following syntax:

To define a non-unique index in PostgreSQL, you can use the following syntax:

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.

Preparing For The Datadog 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. In addition to solving the earlier Datadog SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Microsoft, Google, and Facebook.

Each interview question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an interactive coding environment so you can right in the browser run your SQL query and have it executed.

To prep for the Datadog SQL interview you can also be a great idea to practice interview questions from other tech companies like:

• Splunk SQL questions

• Dynatrace SQL questions

• Zscaler SQL questions

• CrowdStrike SQL questions

But if your SQL foundations are weak, don't worry about diving straight into solving questions – go learn SQL with this interactive SQL tutorial.

This tutorial covers SQL topics like aggregate functions and transforming strings with CONCAT()/LOWER()/TRIM() – both of which come up often in Datadog SQL assesments.

Datadog Data Science Interview Tips

What Do Datadog Data Science Interviews Cover?

For the Datadog Data Science Interview, in addition to SQL query questions, the other types of questions to prepare for are:

• Statistics and Probability Questions
• Python or R Programming Questions
• Open-Ended Data Case Studies
• ML Modelling Questions
• Behavioral Interview Questions

How To Prepare for Datadog Data Science Interviews?

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• 201 interview questions taken from FAANG & startups
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