Window function: rank over category
Last updated: November 20, 2025
Quick Overview
Use window functions to compute running total partitioned by user_id.
Atlassian
November 20, 202568
5
2,168 solved
Use window functions to compute running total partitioned by user_id.
Data manipulation questions at Atlassian test your ability to work with real-world datasets. This Take-home Project question evaluates your SQL proficiency, understanding of data modeling, and ability to derive insights from raw data.
What the Interviewer Expects
- Solve complex analytical problems with elegant, readable SQL
- Optimize queries for large-scale datasets with partitioning and indexing
- Use recursive CTEs, lateral joins, and advanced window functions
- Design the data model alongside the query solution
- Discuss trade-offs between SQL and programmatic approaches (Python/pandas)
- Consider the operational aspects: query scheduling, incremental processing
Key Topics to Cover
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 validate the correctness of your query results?
- Can you rewrite this without using subqueries?
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Solution Pattern
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