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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.

HardSystem Design
41 practiced
Design how the medallion architecture, versioning, and a feature store interact to guarantee reproducible model training datasets such that a model can be retrained in the future and obtain the exact same feature values used in production serving. Describe data versioning, snapshots, and storage choices.
EasyTechnical
46 practiced
What is columnar storage and why do analytical warehouses prefer columnar formats like Parquet or ORC? Explain the benefits in terms of IO reduction, predicate pushdown, vectorized processing, and compression for typical analytics queries.
MediumBehavioral
53 practiced
Behavioral: Tell me about a time you discovered a significant data quality or pipeline issue that impacted a model or dashboard. Describe the situation, how you diagnosed the root cause, how you prioritized remediation, and what you did to prevent recurrence.
MediumTechnical
54 practiced
SQL task: Given a customers_scd2 table with columns (customer_id, customer_key, name, email, effective_from TIMESTAMP, effective_to TIMESTAMP, is_current BOOLEAN), write an ANSI SQL query to produce the current snapshot of all customers (latest record per customer_id). Explain your approach and any assumptions.
EasyTechnical
48 practiced
What is Change Data Capture (CDC) and why is it useful for analytics and model training? Describe two common CDC implementations (log-based CDC and trigger-based) and the pros/cons of each for feeding a data lakehouse.

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