At Palantir, SQL is internally used across the company for extracting and analyzing intelligence and security data. The ability to write SQL queries is even supported inside their Foundry analytics platform. That's why Palantir LOVES to ask SQL questions during interviews for Data Analytics, Data Science, and Forward Deployed Engineer positions.
So, if you're studying for a SQL Assessment, here’s 11 Palantir SQL interview questions to practice, which are similar to commonly asked questions at Palantir – able to answer them all?
As a data analyst at Palantir, you are tasked with the following request. There is a sales table which captures every transaction made by all customers. Write a SQL query that generates a monthly report showing the average sales amount for each product at the product level and the overall level.
sale_id | product_id | sale_date | sale_amount |
---|---|---|---|
22 | 5011 | 3/1/2020 | 55.00 |
125 | 7093 | 3/5/2020 | 35.00 |
3041 | 5011 | 4/14/2020 | 50.00 |
273 | 7093 | 4/20/2020 | 40.00 |
191 | 5011 | 5/10/2020 | 45.00 |
Your query result should return:
month | product_id | avg_sale_amount | avg_sale_amount_overall |
---|---|---|---|
3 | 5011 | 55.00 | 45.00 |
3 | 7093 | 35.00 | 45.00 |
4 | 5011 | 50.00 | 45.00 |
4 | 7093 | 40.00 | 45.00 |
5 | 5011 | 45.00 | 45.00 |
This query is using window functions to calculate the average sales amount. First, it calculates the average sales for each product per month using in the clause. Then it calculates the total average sales per month. The result of both window functions are joined based on the month to get the final report. Note that is used to get the month of the sale from the sale_date.
Palantir Technologies is a public American software company that specializes in big data analytics. It operates by contracting major businesses, NGOs, governments and builds them a database from a large mix of data sources, labelling it as a "platform".
Let's assume one of their product lines consists of various data analytics modules with different versions. They want a way to track the changes in the attributes (features, pricing) of these product modules over different releases.
Design a database schema for this problem. Also, write a SQL query to extract information about the changes in pricing for a particular product module from the database.
module_id | name | version | features | price | release_date |
---|---|---|---|---|---|
1 | 'Data Ingestion' | 'v1.0' | 'Data Import, Data Cleaning' | 1000 | '2022-01-01' |
2 | 'Data Ingestion' | 'v1.1' | 'Data Import, Data Cleaning, API Support' | 1200 | '2022-03-01' |
3 | 'Data Analysis' | 'v2.0' | 'Data Visualization, Report Generation' | 1500 | '2022-02-01' |
4 | 'Data Analysis' | 'v2.1' | 'Data Visualization, Report Generation, Real-time Analysis' | 1700 | '2022-04-01' |
Your task is to return a table showing the module name, version, price, and price change compared to the previous version (if applicable).
This query joins the table with itself based on the module name. It uses the PostgreSQL function to get the price of the previous version of a module (if it exists) and calculates the difference. The final result is sorted by the module name and its release date.
As a final touch, we use the function to handle results from the function in cases where a previous module version does not exist. In these cases, the price change is just the price of the current version.
Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.
For a concrete example, suppose you were a Data Scientist on the Marketing Analytics team at Palantir. A task you would encounter freqently would be to calculate the conversion rate for Palantir's ads over a certain time period, for which you would write the following stored procedure:
To use this conversion rate stored procedure, you could call it like this:
Palantir Technologies Inc. manages a large customer database. They are interested in finding out all the active customers who have made a purchase of over $1000 in total across all their transactions, within the last 30 days.
Here is an example of what the customer records may look like.
Example Input:
transaction_id | customer_id | transaction_date | purchase_amount |
---|---|---|---|
101 | 1546 | 07/01/2022 | 500 |
102 | 2654 | 07/10/2022 | 1200 |
103 | 3321 | 07/11/2022 | 600 |
104 | 1546 | 07/19/2022 | 700 |
105 | 2654 | 07/20/2022 | 800 |
106 | 3321 | 08/01/2022 | 400 |
107 | 1546 | 08/02/2022 | 100 |
108 | 1546 | 08/03/2022 | 400 |
109 | 3321 | 07/01/2022 | 500 |
110 | 2654 | 07/02/2022 | 300 |
Write a SQL query that extracts all customers who have spent more than $1000 within the last 30 days.
Here is the SQL query you can use to solve this problem:
This query does a few things. First, it filters the sales table to only get transactions that occurred within the last month. Then, it groups these records by the customer_id, and finally filters these groups by the total purchase amount of each customer to only get those who have spent over 1000 in total across all their transactions, within the last 30 days.
A is a column or set of columns in a table that references the primary key of another table. It is used to link the two tables together, and to ensure that the data in the foreign key column is valid.
The constraint helps to ensure the integrity of the data in the database by preventing the insertion of rows in the foreign key table that do not have corresponding entries in the primary key table. It also helps to enforce the relationship between the two tables, and can be used to ensure that data is not deleted from the primary key table if there are still references to it in the foreign key table.
For example, if you have a table of Palantir customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the Palantir customers table.
For the company Palantir, which deals with large scale analysis and data integration, a possible interview question using an Average (AVG) function could be:
"Can you write a SQL query to find the average time taken to process a unit of data for each employee on a specific date?”
Suppose we have a table named that logs the data processed by each employee on each date.
log_id | employee_id | process_date | data_unit_id | process_time_min |
---|---|---|---|---|
101 | 1 | 07/01/2022 | 1 | 50 |
102 | 1 | 07/01/2022 | 2 | 60 |
103 | 2 | 07/01/2022 | 3 | 45 |
104 | 2 | 07/01/2022 | 4 | 55 |
105 | 3 | 07/01/2022 | 5 | 35 |
106 | 3 | 07/01/2022 | 6 | 30 |
We want to find the average process time for each employee on "07/01/2022".
employee_id | avg_process_time |
---|---|
1 | 55 |
2 | 50 |
3 | 32.5 |
This query first filters the table for records on the date '07/01/2022' with the WHERE clause. Then it applies the AVG() function to calculate the average process time per each and groups the result by . As a result, we get the average process time for each employee on the given date.
To practice a similar problem about calculating rates, try this TikTok SQL question on DataLemur's online SQL code editor:
Normalization involves dividing a large table into smaller, more specific ones and establishing connections between them. This helps to reduce redundancy, creating a database that is more adaptable, scalable, and easy to manage. Additionally, normalization helps to maintain the integrity of the data by minimizing the risk of inconsistencies and anomalies.
You are given two tables - "Ad_Clicks" and "Product_Cart_Additions". From the data provided, can you calculate both the click-through rate (CTR) for each product and the conversion rate i.e., the percentage of product views that lead to a product being added to the cart? Note that CTR is calculated as (Number of clicks / Number of impressions) * 100% and Conversion Rate is calculated as (Number of products added to the cart / Number of products viewed) * 100%.
Here are the tables:
click_id | user_id | click_date | product_id |
---|---|---|---|
6899 | 23 | 10/08/2021 00:00:00 | 34531 |
8923 | 78 | 10/09/2021 00:00:00 | 23456 |
6445 | 45 | 10/11/2021 00:00:00 | 34531 |
8956 | 34 | 10/12/2021 00:00:00 | 23456 |
7345 | 90 | 11/08/2021 00:00:00 | 34531 |
cart_id | user_id | addition_date | product_id |
---|---|---|---|
9483 | 23 | 10/09/2021 00:00:00 | 34531 |
8923 | 78 | 10/10/2021 00:00:00 | 23456 |
9543 | 34 | 10/13/2021 00:00:00 | 23456 |
8654 | 90 | 11/09/2021 00:00:00 | 34531 |
8342 | 45 | 11/12/2021 00:00:00 | 34531 |
The query first aggregates clicks and cart additions by product_id. It then calculates the CTR and conversion rate by product. The fractions are cast to decimal to prevent integer division, which would result in a 0 for any fraction less than 1. Note - these calculations assume that every ad click is an impression which may not be the case in real world scenarios.
Palantir Technologies offers various data analytics products. As a database administrator, it is important that you can analyze user reviews and ratings of these products.
Given a table with columns , , , and , compute the average rating for each product per year.
review_id | user_id | submit_date | product_id | rating |
---|---|---|---|---|
6575 | 324 | 12/08/2019 | 101 | 5 |
9372 | 489 | 02/12/2020 | 102 | 4 |
1135 | 552 | 06/20/2020 | 103 | 3 |
7345 | 210 | 11/22/2020 | 101 | 4 |
8998 | 987 | 07/07/2021 | 102 | 5 |
year | product | avg_rating |
---|---|---|
2019 | 101 | 5.00 |
2020 | 101 | 4.00 |
2020 | 102 | 4.00 |
2020 | 103 | 3.00 |
2021 | 102 | 5.00 |
This query works by first extracting the year from the . The operation then groups the data by the and . In each group, the function calculates the average rating.
To solve a similar problem about calculating rates, try this TikTok SQL Interview Question on DataLemur's interactive coding environment:
The / 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 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 Palantir should be lenient!).
For a tangible example in PostgreSQL, suppose you were doing an HR Analytics project for Palantir, and had access to Palantir's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables. You could use operator to find all contractors who never were a employee using this query:
The product insights teem at Palantir would like to understand more about user behavior, specifically they want to analyze Foundry users' data and the BI reports they viewed in the table.
Write a SQL query to find out the number of users who viewed a report on the same day as their first login date in 2022.
The table has the following schema:
login_id | user_id | login_date |
---|---|---|
1 | 101 | 01/01/2022 |
2 | 102 | 01/02/2022 |
3 | 103 | 01/05/2022 |
4 | 104 | 01/07/2022 |
5 | 105 | 01/10/2022 |
And the table has the following schema:
report_id | user_id | view_date |
---|---|---|
1 | 101 | 01/01/2022 |
2 | 102 | 01/04/2022 |
3 | 103 | 01/05/2022 |
4 | 104 | 01/08/2022 |
5 | 105 | 01/10/2022 |
Combine these tables in your solution and provide the count of such users.
In this query, we first create a CTE that contains the first login date for each user. We then join the table.
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Palantir SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the earlier Palantir SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each problem on DataLemur has hints to guide you, step-by-step solutions and crucially, there's an online SQL code editor so you can instantly run your SQL query and have it executed.
To prep for the Palantir SQL interview it is also a great idea to solve SQL questions from other tech companies like:
In case your SQL skills are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.
This tutorial covers things like UNION vs. joins and finding NULLs – both of which come up frequently in Palantir SQL assessments.
For the Palantir Data Science Interview, in addition to SQL query questions, the other types of questions which are covered:
Don't gloss over the behavioral interview because the Forward-Deployed nature of Palantir jobs means a heavy focus on people skills and client-relationship management experience.
To prepare for Palantir Data Science interviews read the book Ace the Data Science Interview because it's got: