10 Confluent SQL Interview Questions (Updated 2024)
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 table is formatted as follows:
Example Input:
| event_id | user_id | processed_date | event_type | processed_count |
|---|---|---|---|---|
| 1001 | 762 | 06/08/2022 00:00:00 | Update | 40 |
| 1002 | 543 | 06/10/2022 00:00:00 | Insert | 52 |
| 1003 | 324 | 06/18/2022 00:00:00 | Delete | 35 |
| 1004 | 762 | 07/26/2022 00:00:00 | Update | 60 |
| 1005 | 981 | 07/05/2022 00:00:00 | Update | 43 |
We assume that the top users are those who have processed more than 50 events on any single day.
Answer:
This query groups all records by the , sums up the for each user, and then selects only those users for whom the maximum on any single day is more than 50. The result gives the and total number of events 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.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 00:00:00 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 00:00:00 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 00:00:00 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 00:00:00 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 00:00:00 | 69852 | 2 |
Example Output:
| mth | product | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
This query groups all reviews for each product by month, and then calculates the average star rating for each group. The function is used to pull the month from the timestamp, and is used to calculate the average star rating for each group. Finally, the 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 and functions?
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 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:
Example Input:
| customer_id | name | address |
|---|---|---|
| 1 | John Doe | Address 1 |
| 2 | Jane Smith | Address 2 |
| 3 | Bob Johnson | Address 3 |
| 4 | Alice Williams | Address 4 |
Example Input:
| order_id | customer_id | purchase_date | amount |
|---|---|---|---|
| 1 | 1 | 2021-01-02 00:00:00 | 5000 |
| 2 | 1 | 2021-04-01 00:00:00 | 6000 |
| 3 | 1 | 2021-08-17 00:00:00 | 4500 |
| 4 | 2 | 2022-04-05 00:00:00 | 15000 |
| 5 | 3 | 2021-02-07 00:00:00 | 500 |
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:
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.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, 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, and .
Here is the table:
Example Input:
| visit_id | user_id | visit_date | product_id |
|---|---|---|---|
| 1071 | 110 | 06/01/2022 00:00:00 | 8010 |
| 3242 | 130 | 06/03/2022 00:00:00 | 2019 |
| 5001 | 110 | 07/05/2022 00:00:00 | 8010 |
| 9999 | 140 | 07/10/2022 00:00:00 | 3996 |
| 3243 | 110 | 07/20/2022 00:00:00 | 8010 |
Here is the table:
Example Input:
| add_id | user_id | add_date | product_id |
|---|---|---|---|
| 2001 | 110 | 06/01/2022 00:00:00 | 8010 |
| 3002 | 130 | 06/04/2022 00:00:00 | 2019 |
| 4005 | 110 | 07/06/2022 00:00:00 | 8010 |
| 6007 | 140 | 07/15/2022 00:00:00 | 3996 |
Answer:
Here is the PostgreSQL query to solve your problem:
This query first creates two subqueries, and , 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:
:
+------------+------------+------------+------------+ | 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, could be a primary key. It is unique for each row in the table and cannot contain null values.
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 also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the table could have additional foreign keys for the of the department where each employee works, and the l 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 and .
Example Input:
| topic_id | topic_name |
|---|---|
| 900 | Machine Learning |
| 901 | Database Management |
| 902 | Data Science |
Example Input:
| stream_id | user_id | topic_id | start_time | end_time |
|---|---|---|---|---|
| 6652 | 201 | 900 | 07/01/2022 8:30:00 | 07/01/2022 9:30:00 |
| 5748 | 212 | 900 | 07/01/2022 10:30:00 | 07/01/2022 12:00:00 |
| 4891 | 245 | 902 | 07/02/2022 14:00:00 | 07/02/2022 15:15:00 |
| 7634 | 212 | 901 | 07/03/2022 09:30:00 | 07/03/2022 11:15:00 |
| 8392 | 201 | 902 | 07/04/2022 10:00:00 | 07/04/2022 10:45:00 |
Our goal is to output the following table:
Example Output:
| user_id | topic_name | average_streaming_duration_minutes |
|---|---|---|
| 201 | Machine Learning | 60.00 |
| 212 | Machine Learning | 90.00 |
| 245 | Data Science | 75.00 |
| 212 | Database Management | 105.00 |
| 201 | Data Science | 45.00 |
Answer:
This query first joins the table with the table on . For each combination of and (which is from the topics table), the query calculates the average difference between and (which gives us the streaming duration), and converts this from seconds to minutes using PostgreSQL's function and division by 60. The 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 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'.
Example Input:
| customer_id | first_name | last_name | register_date | |
|---|---|---|---|---|
| 101 | John | Smith | johnsmith@gmail.com | 2018-11-04 |
| 102 | Mike | Johnson | mikejohnson@yahoo.com | 2017-06-10 |
| 103 | Jenna | Brown | jennabrown@hotmail.com | 2021-02-16 |
| 104 | James | Miller | jamesmiller@gmail.com | 2019-07-23 |
| 105 | Jennifer | Wilson | jenniferwilson@yahoo.com | 2020-03-15 |
Answer:
This SQL query will select all columns from the table where the starts with 'J' (denoted by 'J%') and the 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 and ?
The clause is used to filter rows from the result set of a , , or statement. It allows you to specify a condition that must be met for a row to be included in the result set.
The clause is used to filter groups created by the clause. It is similar to the clause, but it is used to specify conditions on the groups created by the 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:
This query retrieves the total impressions and average conversions for each platform in the table, the date of the campaign is in January 2023. The rows are grouped by platform and the 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:
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
- a crash course on SQL, AB Testing & ML
- over 900+ 5-star reviews on Amazon