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Explain the differences between a Data Lake and a Data Warehouse.

SQLeasy0.5 min read

**Data Lake**: Low-cost object storage (S3, ADLS) for raw, semi-structured, unstructured data. Schema-on-read; used for exploratory analytics, ML, archival. **Data Warehouse**: Structured, curated storage optimized for SQL; schema-on-write; used for BI and reporting. **Why both...

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Frequency
Low
Asked at 4 companies
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
ChryselysFedEx DataworksLumiqNAB
Interview Pro Tip

Red Flag: Describing them as mutually exclusive. Pro-Move: Explain the modern pattern—raw lake, curated lakehouse/Delta, and warehouse or semantic layer for specific workloads.

Key Concepts Tested
lakehousesnowflakesql

Why This Question Matters

This easy-level SQL question appears frequently in data engineering interviews at companies like Chryselys, FedEx Dataworks, Lumiq, and 1 others. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (lakehouse, snowflake, sql) will help you answer variations of this question confidently.

How to Approach This

Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.

Expert Answer
94 words

Data Lake: Low-cost object storage (S3, ADLS) for raw, semi-structured, unstructured data. Schema-on-read; used for exploratory analytics, ML, archival. Data Warehouse: Structured, curated storage optimized for SQL; schema-on-write; used for BI and reporting. Why both exist: Lakes offer flexibility and cost at scale; warehouses offer query performance and concurrency. Scalability: Lakes scale storage independently; warehouses scale compute and storage together (or separate in Snowflake). Cost: Lakes ~$20–30/TB; warehouses add compute (per-query or per-second). Trade-off: Lakes require more engineering (governance, quality); warehouses are turnkey but expensive at petabyte scale. Lakehouse (Delta, Iceberg) blurs the line.

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