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

45 practiced

Design a test harness and a set of automated tests to validate the correctness of join logic in a production ETL job that merges orders with customer segments. Tests should cover row counts, duplicate detection, null handling, boundary-time matching, and data lineage. Include SQL assertions and end-to-end test ideas.

MediumTechnical

65 practiced

You encounter a query that joins three large tables without any pre-filtering and returns huge intermediate row counts. Describe three concrete rewrite strategies to reduce intermediate result size and improve performance (e.g., predicate pushdown, filtered subqueries/CTEs, reordering joins). Provide example SQL sketches showing each approach.

EasyTechnical

43 practiced

Given these PostgreSQL tables:

customers(customer_id PK, name text)orders(order_id PK, customer_id FK, placed_at date)

Sample rows:customers: (1,'Alice'),(2,'Bob'),(3,'Cara')orders: (10,1,'2024-01-10'),(11,1,'2024-02-01'),(12,3,'2024-03-05')

Write a single SQL query (PostgreSQL) that returns each customer_id, name, and the number of orders they have (0 when none). Order results by customer_id.

HardTechnical

63 practiced

You maintain a pipeline that joins newly ingested events to a slowly changing dimension (Type 2). Occasionally, duplicate events are emitted and land in the pipeline twice. Describe an end-to-end strategy to ensure the final reporting tables are not double-counted: dedupe incoming events, use deterministic keys for idempotent upserts, and implement monotonic offsets/checkpoints. Provide pseudo-SQL or pseudo-code showing idempotent upsert flow.

MediumTechnical

35 practiced

You need to join customer records from two systems that use slightly different name spellings and sometimes missing IDs. Describe practical approaches to fuzzy-joining these datasets at scale (millions of rows): include blocking, candidate generation (e.g., n-grams, phonetic codes), scoring, and choosing thresholds. Mention tools or DB features (Postgres trigram, Spark, Dedupe library) you would use.

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