Data Warehousing and Data Lakes Questions

Covers conceptual and practical design, architecture, and operational considerations for data warehouses and data lakes. Topics include differences between warehouses and lakes, staging areas and ingestion patterns, schema design such as star schema and dimensional modeling, handling slowly changing dimensions and fact tables, partitioning and bucketing strategies for large datasets, common architectures including medallion architecture with bronze silver and gold layers, real time and batch ingestion approaches, metadata management, and data governance. Interview questions may probe trade offs between architectures, how to design schemas for analytical queries, how to support both analytical performance and flexibility, and how to incorporate lineage and governance into designs.

HardTechnical

57 practiced

Given a large partitioned events table:

events(event_ts TIMESTAMP, user_id STRING, event_type STRING, payload JSON)

Write an optimized SQL (BigQuery/Presto) to compute daily active users (distinct user_id) by event_type for the last 30 days. Explain how your query leverages partition pruning and clustering to avoid full table scans.

HardTechnical

40 practiced

Write a SQL-based approach to maintain materialized daily aggregates for revenue by product_category with near-real-time updates. Show how you'd update aggregates incrementally as new orders arrive, using MERGE statements or equivalent, and consider late events and idempotency.

MediumTechnical

41 practiced

Design a set of automated data quality checks for a daily ETL job that populates the sales fact table. Include checks for completeness, freshness, duplication, referential integrity, and value ranges. Describe alerting behavior and whether pipelines should abort on failures.

EasyTechnical

88 practiced

Explain partitioning in a data warehouse. Describe how partitioning helps query performance and maintenance. Give three example partition keys you might choose for a large time-series sales table and justify each choice.

EasyBehavioral

51 practiced

Tell me about a time you discovered a data discrepancy that materially affected a business report. Walk through the situation using STAR: what happened, your analysis steps, who you engaged, the root cause, and the permanent change you implemented to prevent recurrence.

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