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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
Design a partitioning and clustering strategy for a high-volume events table (estimated 500 billion rows/year) that is commonly queried for daily aggregates by event_date and for user-level histories by user_id. Describe partition key choice, partition size targets, and whether to use bucketing/clustering and why.
HardSystem Design
0 practiced
How would you implement and scale a lineage system that tracks transformations across SQL, Spark, and Python jobs, coordinated by multiple orchestrators, and exposes a searchable API for compliance and impact analysis? Include ingestion of run-time metadata, relationship modeling, and storage choices.
HardSystem Design
0 practiced
Design a metadata and governance system for a lake + warehouse ecosystem that supports fine-grained access control, column-level masking for PII, automated PII discovery/classification, and comprehensive audit logging. Describe components, enforcement points, and integration with IAM and data catalogs.
EasyTechnical
0 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.
MediumTechnical
0 practiced
You need to combine daily incremental ingests from multiple sources into a canonical customers table, but sources use different identifiers (email, phone, external_id). Outline an identity resolution and deduplication strategy that ensures one canonical record per real-world customer, including confidence scoring and handling conflicts.

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