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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
49 practiced
Explain pros and cons of using a managed cloud data warehouse (e.g., BigQuery, Snowflake) vs building a lakehouse on top of object storage with processing engines (e.g., Databricks/Delta, Presto + Iceberg). Focus on operational burden, analytical features, cost predictability, and ecosystem integrations.
EasyTechnical
47 practiced
Your company is deciding whether to use a data lake, a data warehouse, or both. Explain the core architectural and operational differences between a data lake and a data warehouse in the context of analytics, governance, cost, and schema management. Give examples of workloads best suited to each and describe one hybrid approach.
MediumTechnical
41 practiced
Explain the difference between clustering, partitioning, and indexing in the context of large analytical tables. Give examples of when each is effective and how they interact with the underlying file layout in cloud data warehouses.
MediumTechnical
52 practiced
A data pipeline produces many small Parquet files (tens of thousands) causing slow queries. Describe concrete steps to detect, diagnose, and fix the small-file problem in both batch and streaming ingestion scenarios. Include compaction strategies and scheduling considerations.
MediumTechnical
45 practiced
You're designing partitioning for a large fact table (100TB) that is queried by time range and product categories. Explain an appropriate partitioning and bucketing strategy to maximize query performance and minimize small-file problems. Mention how partition pruning and predicate pushdown affect query plans.

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