Reviewed by Aditya Kumar · Last reviewed 2026-03-24
For billions of rows across countries: (1) Partition by country and date—SELECT * FROM sales WHERE country IN ('US','UK') AND sale_date BETWEEN ... enables partition pruning. (2) Use columnar storage (Redshift, BigQuery, Snowflake)—only scan needed columns. (3) Aggregate at...
This medium-level SQL question appears frequently in data engineering interviews at companies like Adidas. 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.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
For billions of rows across countries: (1) Partition by country and date—SELECT * FROM sales WHERE country IN ('US','UK') AND sale_date BETWEEN ... enables partition pruning. (2) Use columnar storage (Redshift, BigQuery, Snowflake)—only scan needed columns. (3) Aggregate at source—pre-aggregate by country/date in a summary table. (4) Use approximate queries (HyperLogLog, APPROX_COUNT_DISTINCT) when exact counts aren't needed. (5) Implement incremental processing—only process new/changed data. (6) Consider materialized views for dashboards. (7) Scale compute (Redshift resize, Snowflake multi-cluster). Example: CREATE MATERIALIZED VIEW mv_sales_by_country AS SELECT country, sale_date, SUM(amount) amt FROM sales GROUP BY 1,2; Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.
Red Flag: Optimizing without EXPLAIN or baseline. Pro-Move: 'EXPLAIN ANALYZE + composite index cut P99 80%; we measured write impact before rollout.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.