Window function: rank over user_id
Last updated: October 13, 2025
Quick Overview
Use window functions to compute running total partitioned by user_id.
Elastic
Data Manipulation (SQL/Python)
Data Scientist
Elastic
October 13, 2025Data Scientist
Take-home Project
Data Manipulation (SQL/Python)
Hard
2
5
4,585 solved
Use window functions to compute running total partitioned by user_id.
Elastic asks this during the Take-home Project 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
- 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
Aggregate functions and GROUP BY
Common Table Expressions (CTEs)
Index optimization and query performance
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
- What indexes would you create to support this query?
- What would you do if this query needs to run every 5 minutes?
- How would you handle this if the data was spread across multiple databases?
- How would you optimize this query for a table with 100 million rows?
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Problem Understanding
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Approach
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