ETL vs ELT: What Interviewers Really Ask
Go beyond the textbook definition. Learn how data engineering interviewers test your understanding of ETL vs ELT and modern data transformation patterns.
Key Takeaways
- ✓The Simple Answer vs The Real Answer
- ✓When to Use Each Approach
- ✓Follow-Up Questions to Expect
The Simple Answer vs The Real Answer
The textbook answer: ETL transforms data before loading, ELT loads raw data first then transforms in the target system.
The real answer interviewers want: Understanding of when each approach makes sense, the trade-offs, and how modern tools like dbt, Spark, and cloud warehouses have shifted the industry toward ELT.
When to Use Each Approach
ETL is better when:
- Data needs cleansing before it hits the warehouse (PII removal, compliance)
- You have limited warehouse compute
- Transformations are complex and need custom code
ELT is better when:
- You have a powerful cloud warehouse (Snowflake, BigQuery, Redshift)
- You want a historical raw data lake
- Multiple teams need the same raw data transformed differently
- You're using dbt for transformations
Follow-Up Questions to Expect
- How does the medallion architecture relate to ELT?
- How do you handle schema evolution in an ELT pipeline?
- What's the role of data quality checks in each approach?
- How do you manage transformations at scale with dbt?
Reviewed by Aditya Kumar · DataEngPrep Editorial Team
Drafted by the editorial team and signed off by Aditya Kumar, founder and lead editor at DataEngPrep. Questions are sourced from real interviews, initial answers are drafted with AI assistance, and every section is human-edited for technical accuracy, relevance to current FAANG hiring rubrics, and clarity. Articles are reviewed periodically as interview patterns evolve.
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