Complex Joins and Set Operations Questions

Focuses on mastering joins and set operations for combining and transforming relational data across multiple tables. Candidates should understand all join types including inner, left, right, full outer, cross joins, self joins, and nested joins, and know when to use each for correctness and performance. This topic also covers set operations such as UNION, INTERSECT, and EXCEPT, differences between joins and set operations, handling duplicates and NULL values correctly, choosing between joins, subqueries, and common table expressions for clarity and efficiency, and reasoning about join order and its performance implications on large tables. Interview questions may include multi table join problems, complex business logic across four or more tables, and scenarios that reveal trade offs between approaches.

HardTechnical

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You must match product SKUs across two systems where SKU may be NULL. Produce SQL that treats NULL SKUs as a separate group (i.e., NULL in both systems should be considered a match only if business requires it). Explain why using COALESCE('NULL', sku) can be dangerous when 'NULL' could be a real SKU and propose safer alternatives.

EasyTechnical

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Describe semi-joins and anti-joins at a practical level. Write two SQL patterns to find customers who have never placed an order: (A) using EXISTS/NOT EXISTS and (B) using LEFT JOIN ... WHERE right.id IS NULL. Explain null-safety concerns with NOT IN.

EasyTechnical

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Explain the differences between UNION, UNION ALL, INTERSECT, and EXCEPT (or MINUS). Include an example: combine two customer email lists to produce unique emails. Explain when to use UNION ALL versus UNION, considering duplicates and performance.

HardTechnical

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Discuss when to use materialized views or pre-joined aggregated tables to optimize expensive multi-table joins in a reporting layer. Cover freshness vs read performance, storage cost, refresh strategies (incremental vs full), concurrency, and handling schema changes that affect dependent views.

HardTechnical

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Data lake tables evolve: a column used for joins may change name or type across partitions. As a Data Analyst writing production joins, how would you make queries resilient to schema evolution? Discuss techniques such as a normalization/view layer, schema registry, versioned views, defensive SQL, and test coverage.

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