Advanced SQL: Window Functions & CTEs for Complex Analysis Questions
Advanced SQL techniques using window functions (ROW_NUMBER, RANK, DENSE_RANK, etc.) and common table expressions (CTEs), including recursive queries, for complex data analysis, ranking and analytics patterns, cumulative totals, and multi-step data transformations within relational databases and data warehousing contexts.
MediumTechnical
0 practiced
You have a large query computing multiple window aggregates (running totals, ranks, percentiles) over a 1B-row table. Describe concrete SQL strategies to optimize runtime: pre-aggregations, materialized views, clustering/partitioning, computing values in a single pass, or rewriting windows as joins. Provide small SQL snippets or pseudo-code illustrating each strategy and when to use them.
HardSystem Design
0 practiced
Design an end-to-end SQL-based feature store pipeline where feature computation uses window functions and CTEs. Include design for scheduling, incremental refresh, backfills, SQL-based validation tests, idempotent upserts, versioning, and monitoring. Provide example SQL snippets for computing features into a staging partition and performing atomic publish (staging swap or upsert). Discuss operational trade-offs.
MediumTechnical
0 practiced
You ingest a near-real-time stream into your warehouse and need to deduplicate events (exactly-once semantics) before feature computation. Describe a SQL-based pattern using staging tables, CTEs, and window functions that achieves idempotent deduplication and supports replays. Include how you would learn which rows changed and how to safely apply updates to the feature tables.
EasyTechnical
0 practiced
You have `purchases(user_id INT, product_id INT, amount DECIMAL, purchased_at TIMESTAMP)`. Return the top 3 products per user by total spend using SQL (tie-handling should preserve deterministic results). Provide product_id, total_spend, and rank per user. Use window functions and CTEs.
MediumTechnical
0 practiced
Design SQL logic to merge sessions across multiple devices for a single user when sessions overlap or are within 5 minutes of each other. Input table `events(user_id, device_id, event_ts)`. Describe step-by-step how to compute merged sessions and provide the core SQL pattern using window functions and CTEs (or recursive CTEs if needed).
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