9 Databricks SQL Interview Questions (Updated 2025)

Databricks loves SQL so much that they made their own Databricks SQL (DBSQL). Because of this, Databricks often tests SQL coding questions during interviews for Data Science, Data Engineering and Data Analytics jobs.

In case you want to ace the SQL Interview, we've curated 9 Databricks SQL interview questions to practice, which are similar to commonly asked questions at Databricks – can you solve them?

9 Databricks SQL Interview Questions

SQL Question 1: Identifying Power Users for Databricks

As a Databricks data analyst, one of the most important tasks is identifying our power users. These are users who frequently use Databricks' tools and services, indicating a high level of engagement. These power users often bring in substantial revenue for the company, making them crucial to identify. For this problem, assume you have been given access to two tables: 'users', containing basic user information, and 'transactions', tracking all their actions in past month. All users who made transactions with a total amount of at least 50000 in past month are considered as power user.

Write a SQL query to find out the information of power users from the 'users' table. Display the user's id, name, and the total amount they spent in past month.

The structure of 'users' and 'transactions' table is given as below:

users Example Input:

transactions Example Input:

Answer:

SELECT u.user_id,
       u.user_name,
       SUM(t.amount) as total_spent_last_month
FROM users u
INNER JOIN transactions t ON u.user_id = t.user_id
WHERE t.date BETWEEN '2022-07-01' AND '2022-07-31'
GROUP BY u.user_id, u.user_name
HAVING SUM(t.amount) >= 50000
ORDER BY total_spent_last_month DESC;

In this query, we join the "users" and "transactions" tables on the "user_id" column. We then filter for transactions within the last month. The GROUP BY clause groups the data according to the user’s id and name. The HAVING clause filters out the power users according to the total amount spent. Finally, we order the result in descending order based on the total amount spent to get the power user at the top.

SQL Question 2: Calculate the Monthly Average Rating for Each Product

For a company like Databricks that might offer multiple products, it's often important to track customer feedback over time. In this question, you're given a 'reviews' table that contains various product reviews submitted by users, with each review having a timestamp (in the form of a submitted date) and a star rating. Your task is to write an SQL query that calculates the average star rating for each product on a monthly basis. Make sure your query returns the month, product_id, and the average stars for each product in that month.

reviews Example Input:

Expected Output:

Answer:

The PostgreSQL query would look something like this.

SELECT 
    EXTRACT(MONTH FROM submit_date) AS month,   
    product_id, 
    AVG(stars) AS avg_stars
FROM 
    reviews 
GROUP BY 
    month, 
    product_id
ORDER BY
    month, 
    product_id;

In the solution, we use the EXTRACT function to get the month from the submit_date column. We then group by the month and product_id, and use the AVG function to calculate the average rating for each product in each month. The ORDER BY clause at the end is used to order the results by month and product_id.

SQL Question 3: How do you identify records in one table that aren't in another?

To discover records in one table that are not present in another, you can utilize a LEFT JOIN and filter out any NULL values in the right-side table.

For example, say you had a table of Databricks customers and a 2nd table of all purchases made with Databricks. To find all customers who did not make a purchase, you'd use the following LEFT JOIN

SELECT *
FROM customers
LEFT JOIN purchases ON customers.id = purchases.customer_id
WHERE purchases.id IS NULL;

This query fetches all rows from the customers table, along with any rows that match in the purchases table. If there is no matching row in the purchases table, NULL values will be returned for all of the right table's columns. The WHERE clause then filters out any rows where the purchases.id column is NULL, leaving only customers who have not made a purchase.

SQL Question 4: Database Design for Databricks Machine Learning Jobs

Databricks is a leading data analytics platform that also offers robust machine learning capabilities. Your role will involve designing a database for tracking ML jobs running on the platform.

There would be two main entities - Jobs and Users. A single user can create multiple jobs, so there is a one-to-many relationship between Users and Jobs.

The following are the suggested attributes (columns) for each entity:

• Users: user_id, account_created_date, last_login
• Jobs: job_id, user_id, launched_date, description, status

The status attribute in Jobs table could have values like 'In Progress', 'Completed', or 'Failed'.

Create a solution to track the last 3 jobs each user launched and their status. The output should be sorted by the launched_date in a descending order.

users Example Input:

jobs Example Input:

Answer:

WITH ranked_jobs AS (
    SELECT job_id, user_id, launched_date, description, status,
    ROW_NUMBER() OVER(PARTITION BY user_id ORDER BY launched_date DESC) as rn
    FROM jobs
)
SELECT u.user_id, u.account_created_date, 
rj.job_id, rj.launched_date, rj.description, rj.status
FROM users u
LEFT JOIN ranked_jobs rj
ON u.user_id = rj.user_id
WHERE rj.rn <= 3
ORDER BY u.user_id, rj.launched_date DESC;

This query first creates a temporary view of the jobs table, adding a new column 'rn' that ranks jobs launched by the same user in descending order of their launched_date. Then it performs a left join between the users and this ranked jobs view, filtering for rows where 'rn' is less than or equal to 3. This allows us to get the last 3 jobs launched by each user. The final result set is ordered by user_id and launched_date in descending order.

To practice a related SQL interview question on DataLemur's free online SQL code editor, attempt this Meta SQL interview question:

SQL Question 5: Can you explain what MINUS / EXCEPT SQL commands do?

The MINUS/EXCEPT operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.

Note that EXCEPT is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't stress about knowing which DBMS supports what exact commands since the interviewers at Databricks should be lenient!).

Here's a PostgreSQL example of using EXCEPT to find all of Databricks's Facebook video ads with more than 10k views that aren't also being run on YouTube:

SELECT ad_creative_id
FROM databricks_facebook_ads
WHERE views > 10000 AND type=video

EXCEPT

SELECT ad_creative_id
FROM databricks_youtube_ads

If you want to retain duplicates, you can use the EXCEPT ALL operator instead of EXCEPT. The EXCEPT ALL operator will return all rows, including duplicates.

SQL Question 6: Filter Customer Records Based on Multiple Conditions

Given a database of customer records, your task is to filter the records based on certain conditions. You will need to list customers who signed up after 2018-01-01, are located in New York, and have spent over 5000 units of currency on products in the Electronics department.

customer Example Input:

purchase Example Input:

Example Output:

Answer:

To get the necessary data, you would need to join the customer and purchase tables, and then filter based on the given conditions.

Here is the PostgreSQL code that will accomplish this:

SELECT c.customer_id, c.signup_date, c.location, c.total_spend
FROM customer AS c
JOIN purchase AS p
ON c.customer_id = p.customer_id
WHERE c.signup_date > '2018-01-01'
AND c.location = 'New York'
AND p.department = 'Electronics'
AND p.amount > 5000;

In this SQL query, we first join the customer table with the purchase table where their customer_id fields match. Then, we filter out the customers who meet all of the specified conditions using the WHERE clause.

To practice another question about calculating rates, solve this TikTok SQL question within DataLemur's online SQL code editor:

SQL Question 7: What's a database view, and what's it used for?

A database view is a virtual table that is created based on the results of a SELECT statement, and provides you a customized, read-only version of your data that you can query just like a regular table.

Views in SQL can help you enforce data security requirements by hiding sensitive data from certain users, and can improve performance for some queries by pre-computing the results for an intermediate step and storing them in a view (which can be faster than executing the intermediate query each time). However, their read-only nature means that on any underlying update, the view has to be re-computed.

SQL Question 8: Calculate the Product Page Click-through to Adding to Cart Conversion Rate

As Databricks, we are very interested in understanding our product engagement. We have a table called "page_views" which represents product page views by customers, and another table "cart_adds" that represents every time a user adds a product into their shopping cart.

The "page_views" table has the following schema:

• view_id (integer): unique identifier of the page view
• user_id (integer): unique identifier of the user
• view_date (date): the date when the page view occurred
• product_id (integer): unique identifier of the viewed product

And the "cart_adds" table has the similar schema:

• add_id (integer): unique identifier of the cart addition
• user_id (integer): unique identifier of the user
• add_date (date): the date when the product was added to the cart
• product_id (integer): unique identifier of the added product

We want to know the clickthrough conversion rate from viewing a product to adding that product to the cart. Create a SQL query that calculates the percentage of product page views which resulted in the same product being added to the cart, per product_id.

page_views Example Input:

cart_adds Example Input:

Answer:

SELECT 
    pv.product_id,
    coalesce(count(ca.add_id)*100.0/count(pv.view_id), 0) as conversion_rate
FROM page_views as pv
LEFT JOIN cart_adds as ca
ON pv.user_id = ca.user_id AND pv.product_id = ca.product_id AND pv.view_date <= ca.add_date
GROUP BY pv.product_id

This query calculates the clickthrough conversion rate by joining the tables on user_id and product_id, with the date condition that the addition should happen after the view. We use coalesce to handle any NULL values occurring when a product_id from 'page_views' doesn't exist in 'cart_adds', setting the conversion_rate to 0 in those cases.

To solve a similar problem about calculating rates, solve this TikTok SQL question within DataLemur's interactive SQL code editor:

SQL Question 9: Filtering Client Data From Databricks

As a data professional at Databricks, your task is to filter through the client database to find records specific to client names that start with a given pattern. In this case, we would like you to retrieve all records of clients from the database whose names start with 'Data'.

Below is a sample table named clients hosting details such as client_id, client_name, join_date and client_type.

clients Example Input:

Write a SQL command to pull records of clients whose name begins with 'Data'.

Answer:

SELECT *
FROM clients
WHERE client_name LIKE 'Data%';

This SQL query uses the LIKE keyword to filter the client_name column. The 'Data%' is a pattern that matches any client names that start with 'Data'. The '%' symbol is a wildcard that matches zero or more characters, so any client_name that begins with 'Data' followed by any character sequence will be returned by this query.

How To Prepare for the Databricks SQL Interview

To prepare, review Databricks SQL (DBSQL) reference docs and also this DataLemur SQL tutorial.

You should also practice, practice, and then practice some more to truly ace the Databricks SQL interview. Try the 200 tricky SQL questions on DataLemur which come from companies like FAANG tech companies and tech startups.

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

You can also be wise to solve SQL questions from other tech companies like:

• Snowflake SQL questions
• Amazon SQL questions
• Datadog SQL questions
• Microsoft SQL questions

Databricks Data Engineer Interview

Databricks Data Engineering interviews ask SQL interview questions, Data Structures & Algorithm coding questions, and also general DE questions like:

• given a SQL query, figure out the query plan and what optimizations can be done to improve it?
• how many notebooks have you written that are in production?
• how did you source control your development of notebooks?
• what is the biggest dataset you have created with data bricks?
• what is the longest running notebook you have created?
• what is the biggest cluster you've used?
• what is the largest data frame you have broadcast?

Databricks Data Science Interview Tips

What Do Databricks Data Science Interviews Cover?

For the Databricks Data Science Interview, in addition to SQL query questions, the other types of questions to practice:

• Statistics and Probability Questions
• Python or R Programming Questions
• Business Sense and Product-Sense Questions
• Machine Learning and Predictive Modeling Questions
• Resume-Based Behavioral Questions

How To Prepare for Databricks Data Science Interviews?

The best way to prepare for Databricks Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
• A Refresher on Stats, SQL & ML
• Amazing Reviews (900+ 5-star reviews on Amazon)