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.
HardSystem Design
0 practiced
Design a cost-conscious reporting pipeline to join multi-terabyte event logs with dimension tables to produce daily aggregates. Requirements: incremental runs, reproducible results, queries finish within 2 hours, and schema drift detection for event logs. Describe storage layout (format/partitioning), pre-aggregation vs late join trade-offs, and orchestration choices.
MediumTechnical
0 practiced
In PySpark DataFrame API, show how to join two DataFrames where dfA has event_time and dfB has ranges [start_ts, end_ts) and you need to assign the row from dfB whose interval contains the event_time. Provide code snippet and explain how to avoid accidental cartesian joins at scale.
EasyTechnical
0 practiced
Given table employees(employee_id PK, manager_id FK nullable, name varchar), write a SQL query to return each employee with their immediate manager's name (NULL if top-level). Also describe how you would detect cycles in the manager relationship (e.g., A -> B -> A). Use PostgreSQL syntax where helpful.
HardTechnical
0 practiced
Compare how BigQuery, Snowflake, and Redshift execute joins and list tuning knobs each exposes for complex joins (clustering, distribution keys, sort keys, result caching). For multi-condition joins and date-range joins, give provider-specific recommendations to improve performance and reduce costs.
EasyTechnical
0 practiced
Two partner systems export customer identifiers differently: System A uses uppercase UUIDs, System B uses hyphenated lowercase UUIDs and sometimes adds a prefix. As a data engineer, outline the pragmatic approaches to reliably join the two datasets and the trade-offs (performance, maintainability, data integrity).
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