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Home/Questions/SQL/Describe how you would implement Slowly Changing Dimensions (SCD) in an ETL workflow.

Describe how you would implement Slowly Changing Dimensions (SCD) in an ETL workflow.

SQLeasy0.5 min readPremium

**Architectural Logic**: SCD implementation depends on type; MERGE is the workhorse for atomicity. **SCD1**: UPDATE dim SET col = new_val WHERE key = X. **SCD2**: INSERT new row with effective_from; UPDATE current row's effective_to; set current_flag. **SCD3**: Add prev_col; on...

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Frequency
Low
Asked at 1 company
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
BCG
Key Concepts Tested
etl

Why This Question Matters

This easy-level SQL question appears frequently in data engineering interviews at companies like BCG. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl) will help you answer variations of this question confidently.

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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
99 words

Architectural Logic: SCD implementation depends on type; MERGE is the workhorse for atomicity. SCD1: UPDATE dim SET col = new_val WHERE key = X. SCD2: INSERT new row with effective_from; UPDATE current row's effective_to; set current_flag. SCD3: Add prev_col; on change, shift current→prev. ETL Flow: (1) Extract changed records. (2) Lookup dimension. (3) Apply SCD logic (MERGE or INSERT/UPDATE). (4) Generate surrogate keys. Hash Comparison: For Type 2, hash attributes to detect change. Scalability: Use staging for change detection; batch MERGE. Cost: SCD2 increases row count; plan for history growth. Best Practice: Audit columns (created_at, updated_by); handle late-arriving dimensions.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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