Window function: lead/lag over user_id
Last updated: August 13, 2025
Quick Overview
Use window functions to compute rank partitioned by date.
Amazon
Data Manipulation (SQL/Python)
Data Scientist
Amazon
August 13, 2025Data Scientist
Onsite
Data Manipulation (SQL/Python)
Hard
8
4
4,237 solved
Use window functions to compute rank partitioned by date.
Amazon asks this during the Onsite 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
JOIN types and when to use each
Aggregate functions and GROUP BY
NULL handling and COALESCE
Index optimization and query performance
Pandas vectorized operations and groupby
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?
- How would you validate the correctness of your query results?
- 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?
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Practice SQL ProblemsSample Answer
Problem Understanding
The problem involves calculating the rank of users based on their activity over a specified date range. The dataset includes user activity data with at least the following columns: user_id, `activit...
Approach
- Identify the Columns: Start by selecting the necessary columns:
user_id,activity_date, andactivity_score. - Ranking Logic: Use the SQL window function
RANK()to generate a rank f...
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