Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Multiple tables + window: join fact to dims, then apply window (PARTITION BY dim, ORDER BY date). Example: SELECT f.*, d.name, SUM(f.amount) OVER (PARTITION BY f.customer_id ORDER BY f.date) running_total FROM fact f JOIN dim_customer d ON f.customer_id = d.id. Use CTEs: with...
Multiple tables + window: join fact to dims, then apply window (PARTITION BY dim, ORDER BY date). Example: SELECT f., d.name, SUM(f.amount) OVER (PARTITION BY f.customer_id ORDER BY f.date) running_total FROM fact f JOIN dim_customer d ON f.customer_id = d.id. Use CTEs: with joined as (select ... joins), windowed as (select , row_number() over (...) from joined) select * from windowed where ... 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: Generic textbook answers. Pro-Move: 'At scale we measured X, implemented Y, achieved Z%—validated and iterated.'
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