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.
MediumTechnical
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Describe how to perform an UPSERT/MERGE in a data warehouse for integrating incremental source data that must be joined to existing dimension records. Discuss concurrency, idempotency, and race-condition avoidance. Show SQL for MERGE (Snowflake/BigQuery/SQL Server style) or a safe multi-step strategy where MERGE is not available.
MediumTechnical
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You have customers and multiple addresses per customer with effective_from timestamps. For each order, attach the customer's most recent address as of order_time. Provide a SQL solution that deduplicates addresses per customer using window functions before joining to orders, ensuring one matched address per order even when addresses change frequently.
HardTechnical
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Read and interpret this simplified EXPLAIN ANALYZE output from PostgreSQL (example):Hash Join (cost=... rows=10000 width=64) Hash Cond: (a.user_id = b.user_id) -> Seq Scan on a (rows=1000000) -> Hash (cost=... rows=100000) -> Seq Scan on b (rows=100000)Explain what the plan indicates about join algorithm, estimated row counts, and where you would look to improve performance. What does it mean if actual rows differ greatly from estimated rows?
HardTechnical
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Both your events table and dimension table store validity windows (business time) and you also keep system-time for auditing (bi-temporal). Describe how to write a query that joins events to the dimension rows that were business-valid at event_time while also selecting the dimension version with the correct system-time (e.g., the dimension version visible at a reporting snapshot). Explain assumptions and SQL patterns.
MediumTechnical
0 practiced
You need to join orders to a price_list that is valid for date ranges: price_list(product_id, price, valid_from date, valid_to date). Write a standard SQL query to attach the correct price for each order placed_at date. Describe the inclusive/exclusive considerations for boundaries and show preferred handling to avoid ambiguity.
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