10 Confluent SQL Interview Questions (Updated 2025)

At Confluent, SQL is used frequently for analyzing streaming data in real-time from Kafka pipelines via their own SQL dialect KSQL. That's why Confluent typically asks SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.

So, to help you study for the Confluent SQL interview, this blog covers 10 Confluent SQL interview questions – can you answer each one?

10 Confluent SQL Interview Questions

SQL Question 1: Identifying Top Confluent Users

Confluent, a real-time data streaming platform, wants to identify its top users in terms of the frequency of events processed. These are considered "whale users", or those who perform key activities significantly more than their counterparts. Write a SQL query that identifies these key users along with the total number of events processed by them.

The events table is formatted as follows:

events Example Input:

We assume that the top users are those who have processed more than 50 events on any single day.

Answer:

SELECT user_id,
       SUM(processed_count) as total_processed
FROM events
GROUP BY user_id
HAVING MAX(processed_count) > 50;

This query groups all records by the user_id, sums up the processed_count for each user, and then selects only those users for whom the maximum processed_count on any single day is more than 50. The result gives the user_id and total number of events processed (total_processed) for all top users of Confluent.

To practice a similar VIP customer analysis question on DataLemur's free online SQL code editor, try this Microsoft SQL Interview problem:

SQL Question 2: Find Monthly Average Ratings Per Product

Imagine you are working for Confluent and you are tasked to analyze the ratings given by customers to each product for each month. The reviews are stored in a table named "reviews". The table has the following schema:

• review_id (int): The unique identifier for each review.
• user_id (int): The identifier for the user who wrote the review.
• submit_date (timestamp): The date and time when the review was submitted.
• product_id (int): The identifier for the product that the review is for.
• stars (int): The rating (from 1 to 5) given by the user to the product.

Write a SQL query that returns the monthly average star rating per product. The return result set should have the schema as follows:

• mth (int): The month the reviews were submitted.
• product (int): The product_id.
• avg_stars (float): The average star rating of the product for that month.

reviews Example Input:

Example Output:

Answer:

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

This query groups all reviews for each product by month, and then calculates the average star rating for each group. The EXTRACT function is used to pull the month from the submit_date timestamp, and AVG is used to calculate the average star rating for each group. Finally, the ORDER BY clause is used to sort the result by month and product for easier analysis.

For more window function practice, try this Uber SQL Interview Question within DataLemur's interactive SQL code editor:

SQL Question 3: What are the similarities and differences between the LEAD() and LAG() functions?

Both the LEAD() and LAG() window functions are used to access a row at a specific offset from the current row.

However, the LEAD() function retrieves a value from a row that follows the current row, whereas the LAG() 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:

SELECT id, value,
  LEAD(value, 1) OVER (ORDER BY id) as next_value,
  LAG(value, 1) OVER (ORDER BY id) as prev_value
FROM confluent_purchases;

SQL Question 4: Find top tier customers

Confluent would like to identify their top tier customers. They categorize a top tier customer as one that has spent more than $15000 and has placed at least 3 orders a year. They are particularly interested in seeing data for the year 2021. The customer and orders tables are structured as shown below:

customer Example Input:

orders Example Input:

Given the structure of the two tables, write a SQL query that selects the names of the top tier customers for the year 2021.

Answer:

SELECT c.name
FROM customer c
INNER JOIN (
	SELECT customer_id
	FROM orders o
	WHERE extract(year from o.purchase_date) = 2021
	GROUP BY customer_id
	HAVING SUM(o.amount) > 15000 AND COUNT(o.order_id) >= 3
) ord
ON c.customer_id = ord.customer_id;

The subquery in the above query groups the orders table by customer_id for the year 2021 and selects only those customers who have spent more than $15000 and placed at least 3 orders. The main query then takes the output of the subquery and joins it with the customer table to get the names of top tier customers.

SQL Question 5: Can you explain the distinction between an inner and a full outer join?

An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.

For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Confluent sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.

INNER JOIN: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the Advertising_Campaigns table and the Sales table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the Advertising_Campaigns table matches the keyword in the Sales table.

FULL OUTER JOIN: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.

SQL Question 6: Analyzing Click-through-Rates

Confluent, an event streaming platform, might be interested in understanding the click-through conversion rates from visiting a product page to adding a product to the cart. As a data professional at Confluent, your task is to calculate the click-through rates for each product on a monthly basis.

We have data coming from two tables, page_visits and cart_adds.

Here is the page_visits table:

page_visits Example Input:

Here is the cart_adds table:

cart_adds Example Input:

Answer:

Here is the PostgreSQL query to solve your problem:

WITH monthly_visits AS (
    SELECT DATE_TRUNC('month', visit_date) AS visit_mth, product_id, COUNT(*) as visits
    FROM page_visits
    GROUP BY visit_mth, product_id),

monthly_adds AS (
    SELECT DATE_TRUNC('month', add_date) AS add_mth, product_id, COUNT(*) as adds
    FROM cart_adds
    GROUP BY add_mth, product_id)

SELECT v.visit_mth, v.product_id, v.visits, a.adds, (a.adds::decimal / v.visits::decimal) * 100 AS click_through_rate
FROM monthly_visits v
JOIN monthly_adds a ON v.visit_mth = a.add_mth AND v.product_id = a.product_id;

This query first creates two subqueries, monthly_visits and monthly_adds, that group the raw event data by month and product. The main query then joins these two subqueries on the month and product, and calculates the click-through rate by dividing the number of adds by the number of visits per month per product.

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

SQL Question 7: What's the difference between a foreign and primary key?

To explain the difference between a primary key and foreign key, let's inspect employee data from Confluent's HR database:

confluent_employees:

+------------+------------+------------+------------+ | 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, employee_id could be a primary key. It is unique for each row in the table and cannot contain null values.

manager_id could be a foreign key. It references the employee_id 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 also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the confluent_employees table could have additional foreign keys for the department_id of the department where each employee works, and the location_id of the location where each employee is based.

SQL Question 8: Calculate the Average Stream Duration per User for Each Topic

Confluent Inc. is a company that provides a real-time data streaming platform. Let's assume in this scenario that users are streaming different topics, and we want to find out the average streaming duration (in minutes) per user for each topic.

We have two tables topics and streaming_activities.

topics Example Input:

streaming_activities Example Input:

Our goal is to output the following table:

Example Output:

Answer:

SELECT sa.user_id, t.topic_name, AVG(EXTRACT(EPOCH FROM (sa.end_time - sa.start_time))/60) AS average_streaming_duration_minutes
FROM streaming_activities sa
JOIN topics t ON sa.topic_id = t.topic_id
GROUP BY sa.user_id, t.topic_name;

This query first joins the streaming_activities table with the topics table on topic_id. For each combination of user_id and topic_name (which is from the topics table), the query calculates the average difference between end_time and start_time (which gives us the streaming duration), and converts this from seconds to minutes using PostgreSQL's EXTRACT function and division by 60. The GROUP BY clause breaks down this average calculation per user for each topic.

SQL Question 9: Filtering customer records with a pattern match

Given a table named customers containing customer records for the company Confluent, write an SQL query to find all customer records whose first name starts with 'J' and their email contains 'gmail'.

customers Example Input:

Answer:

SELECT *
FROM customers
WHERE first_name LIKE 'J%' AND email LIKE '%gmail.com'

This SQL query will select all columns from the customers table where the first_name starts with 'J' (denoted by 'J%') and the email contains 'gmail.com' (denoted by '%gmail.com'). The '%' is a wildcard character that matches any sequence of characters.

SQL Question 10: What's the major difference between WHERE and HAVING?

The WHERE clause is used to filter rows from the result set of a SELECT, UPDATE, or DELETE statement. It allows you to specify a condition that must be met for a row to be included in the result set.

The HAVING clause is used to filter groups created by the GROUP BY clause. It is similar to the WHERE clause, but it is used to specify conditions on the groups created by the GROUP BY clause, rather than on the individual rows of the table.

Say you were working on a social media analytics project for Confluent.

Here is an example of a SQL query that you might write which uses both the WHERE and HAVING clauses:

SELECT platform, SUM(impressions) AS total_impressions, AVG(conversions) AS avg_conversions
FROM confluent_social_media_data
WHERE date >= '2023-01-01' AND date < '2023-02-01'
GROUP BY platform
HAVING SUM(impressions) > 5000 AND AVG(conversions) > 0.2;

This query retrieves the total impressions and average conversions for each platform in the confluent_social_media_data table, WHERE the date of the campaign is in January 2023. The rows are grouped by platform and the HAVING clause filters the groups to include only those with more than 5000 impressions and an average conversion rate above 0.2.

Confluent SQL Interview Tips

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Confluent SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the earlier Confluent SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.

Each problem on DataLemur has multiple hints, full answers and most importantly, there is an interactive coding environment so you can right in the browser run your SQL query and have it graded.

To prep for the Confluent SQL interview you can also be useful to solve interview questions from other tech companies like:

• MongoDB SQL questions

• Databricks SQL questions

• Snowflake SQL questions

• Cloudera SQL questions

In case your SQL query skills are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers SQL topics like filtering groups with HAVING and LEAD/LAG – both of which come up often in SQL interviews at Confluent.

Confluent Data Science Interview Tips

What Do Confluent Data Science Interviews Cover?

In addition to SQL query questions, the other topics to prepare for the Confluent Data Science Interview are:

• Probability & Stats Questions
• Python or R Programming Questions
• Product Data Science Interview Questions
• ML Interview Questions
• Behavioral Based Interview Questions

How To Prepare for Confluent Data Science Interviews?

To prepare for Confluent Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions sourced from FAANG & startups
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