Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**SQL (full row)**: `SELECT * FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY sale_timestamp DESC) AS rn FROM sales) t WHERE rn = 1` — Returns the full record. **SQL (timestamp only)**: `SELECT product_id, MAX(sale_timestamp) FROM sales GROUP BY product_id` —...
Red Flag: Using `DISTINCT ON` (Postgres) or `LAST_VALUE` without specifying frame—different engines have different semantics. Pro-Move: 'We use ROW_NUMBER because we need the full transaction record. We partition by product_id and cluster the table by product_id to minimize shuffle; we've seen 3x improvement over a naive GROUP BY + join.'
This hard-level General/Other question appears frequently in data engineering interviews at companies like Presidio, Swiggy, Stripe. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (bigquery, partition, snowflake) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
SQL (full row): SELECT FROM (SELECT , ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY sale_timestamp DESC) AS rn FROM sales) t WHERE rn = 1 — Returns the full record. SQL (timestamp only): SELECT product_id, MAX(sale_timestamp) FROM sales GROUP BY product_id — Use when you only need the timestamp. PySpark: df.withColumn('rn', F.row_number().over(Window.partitionBy('product_id').orderBy(F.desc('sale_timestamp')))).filter(F.col('rn') == 1).drop('rn') — Full row. For aggregation: df.groupBy('product_id').agg(F.max('sale_timestamp').alias('latest_sale')). Scalability trade-off: ROW_NUMBER requires a sort within each partition—O(n log n) per partition. On large datasets, ensure product_id is well-distributed to avoid skew. If one product has 90% of rows, that partition dominates. Consider salting or bucketing. Cost: In BigQuery/Snowflake, MAX with GROUP BY is often more efficient than window functions for large scans; use ROW_NUMBER when you need other columns from the winning row.
Red Flag: Using DISTINCT ON (Postgres) or LAST_VALUE without specifying frame—different engines have different semantics. Pro-Move: 'We use ROW_NUMBER because we need the full transaction record. We partition by product_id and cluster the table by product_id to minimize shuffle; we've seen 3x improvement over a naive GROUP BY + join.'
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According to DataEngPrep.tech, this is one of the most frequently asked General/Other interview questions, reported at 3 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.