Window function: running total over date
Last updated: December 18, 2025
Quick Overview
Use window functions to compute rank partitioned by date.
Vercel
Data Manipulation (SQL/Python)
Data Scientist
Vercel
December 18, 2025Data Scientist
Technical Screen
Data Manipulation (SQL/Python)
Easy
6
6
1,784 solved
Use window functions to compute rank partitioned by date.
Vercel asks this during the Technical Screen because data engineering skills are critical for the role. You should be comfortable with complex joins, window functions, CTEs, and performance optimization.
What the Interviewer Expects
- Write syntactically correct SQL with proper JOIN and WHERE clauses
- Use GROUP BY and aggregate functions appropriately
- Handle NULL values correctly in your queries
- Explain the query execution plan at a high level
Key Topics to Cover
Common Table Expressions (CTEs)
Date/time manipulation
Aggregate functions and GROUP BY
NULL handling and COALESCE
How to Approach This
- Clarify the schema and expected output format before writing queries.
- Use CTEs (WITH clauses) to break complex queries into readable steps.
- Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
- Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
- For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
- How would you optimize this query for a table with 100 million rows?
- How would you handle slowly changing dimensions in this scenario?
- How would you handle this if the data was spread across multiple databases?
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Practice SQL ProblemsSample Answer
Problem Understanding
The task is to compute a running total (cumulative sum) of a certain metric (e.g., sales) partitioned by date. This involves using window functions to create a cumulative total that resets for each da...
Approach
- Identify the data source: Determine the table that contains the date and metric data.
- Select necessary columns: Use
SELECTto choose thedateand the metric column (e.g.,sales). 3...
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