AdvancedTechnical
5 min
Data Lake vs Data Warehouse
Data EngineeringAnalyticsArchitecture
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Interview Question
Compare data lakes and data warehouses in terms of architecture, use cases, and trade-offs.
Key Points to Cover
- Data lake: raw, schema-on-read, flexible, unstructured data
- Data warehouse: structured, schema-on-write, optimized for BI
- Trade-offs in cost, performance, and agility
- Modern trend: lakehouse combining both
Evaluation Rubric
Explains data lakes clearly30% weight
Explains data warehouses clearly30% weight
Compares trade-offs effectively20% weight
Mentions lakehouse evolution20% weight
Hints
- 💡Think AWS S3 vs Snowflake/BigQuery.
Common Pitfalls to Avoid
- ⚠️Failing to implement proper data governance and metadata management for data lakes, leading to a 'data swamp'.
- ⚠️Over-engineering data warehouses with overly complex schemas that hinder agility for emerging analytical needs.
- ⚠️Assuming one solution fits all use cases without considering the distinct strengths of each architecture.
- ⚠️Underestimating the cost of data processing and transformation required for data lakes to become analytically useful.
- ⚠️Ignoring the security and access control implications of storing vast amounts of raw data in a data lake.
Potential Follow-up Questions
- ❓When to choose one over the other?
- ❓What’s schema drift?
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