Complex Data Integration and Joins Questions

Handling intricate join scenarios: multi-condition joins, conditional joins with complex logic, joining on date ranges or overlapping time periods, complex left joins with multiple filtering conditions, self-joins for hierarchical or relationship data, handling non-standard relationships between tables. Understanding implications of different join types on row counts, NULL values, and duplicate handling. Designing queries that correctly integrate data from multiple sources while maintaining data integrity and avoiding duplicate counting or missing data.

HardTechnical

46 practiced

Explain a scalable approach in Apache Spark to join orders to promotions (time ranges) while minimizing shuffle and avoiding cartesian explosion. Describe code-level choices in Spark 3.x (broadcast, repartition, map-side join), partitioning strategy, and fallbacks when promotions are not small.

MediumSystem Design

41 practiced

Design a daily pipeline to integrate product catalogs from multiple suppliers with overlapping SKUs. Requirements: ingest raw sources, preserve original attributes, detect and merge duplicate products into canonical rows, allow human review for ambiguous matches, and publish a canonical product table. Describe components, matching strategy (exact + fuzzy), tooling choices (e.g., Spark, Airflow), and testing approach.

EasyTechnical

36 practiced

Using PostgreSQL, write a SQL query that returns every customer and their most recent order_date (or NULL if none). Schemas: customers(customer_id PK, name), orders(order_id PK, customer_id FK, order_date date, status varchar). Important: keep customers who have no orders and avoid turning the LEFT JOIN into an INNER JOIN when filtering on orders.status = 'completed'. Provide the query and a brief explanation of your approach.

HardTechnical

69 practiced

Provide a systematic debugging checklist for missing rows after complex joins across three datasets. For each checklist item, give the concrete SQL check you would run and a short explanation of what a suspicious result would indicate. Cover schema mismatches, join key distributions, implicit casts, nulls, timezone mismatches, and data freshness.

MediumTechnical

44 practiced

Write a PostgreSQL query that joins orders(order_id, product_id, order_date, qty) to price_history(product_id, price, effective_from date, effective_to date nullable) to pick the price valid at the order_date. Handle NULL effective_to as current (open-ended) and assume there may be overlapping ranges; define how you resolve overlaps.

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