Window function: lead/lag over category
Last updated: August 22, 2025
Quick Overview
Use window functions to compute rank partitioned by user_id.
Workday
Data Manipulation (SQL/Python)
Data Scientist
Workday
August 22, 2025Data Scientist
Take-home Project
Data Manipulation (SQL/Python)
Hard
25
11
680 solved
Use window functions to compute rank partitioned by user_id.
This question from Workday's Take-home Project tests practical data skills. The interviewer wants to see clean, efficient queries that handle edge cases like NULLs, duplicates, and large datasets.
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
Common Table Expressions (CTEs)
Data cleaning and transformation
Index optimization and query performance
Pandas vectorized operations and groupby
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
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 handle slowly changing dimensions in this scenario?
- How would you optimize this query for a table with 100 million rows?
- How would you handle this if the data was spread across multiple databases?
- What indexes would you create to support this query?
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Practice SQL ProblemsSample Answer
Problem Understanding
The task requires using window functions to compute the rank of entries partitioned by user_id. The data will typically involve a table that contains user activity logs, including columns such as `u...
Approach
- Identify the Dataset: Determine the structure of the activity logs table, including relevant columns.
- Use Common Table Expressions (CTE): This will help in breaking down the query into...
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