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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.

MediumTechnical
0 practiced
Explain Change Data Capture (CDC) and how it is used to keep a data warehouse in sync with transactional systems. As an analyst, what artifacts would you expect in the warehouse when CDC is applied, and how would you use CDC to keep dimension tables up-to-date?
MediumTechnical
0 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.
HardTechnical
0 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.
EasyTechnical
0 practiced
Explain the differences between a data warehouse and a data lake. In your answer, discuss typical storage formats, query patterns, primary users (analysts vs data scientists), latency and freshness expectations, and give two concrete use cases where one is preferable over the other.
HardSystem Design
0 practiced
Design an end-to-end data lineage and governance solution that captures dataset-level and column-level lineage across scheduled ETL jobs, streaming consumers, and BI dashboards. Describe metadata capture, storage, access control, analyst workflows, and how you'd enforce data contracts.

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