Explain slowly changing dimensions and SCD Type 2.
Interviewer Expectations for this Question
01
Core Competency
Assesses fundamental understanding of Data Engineering conventions, runtime behavior, and memory/performance considerations.
02
Evaluation Criteria
Hiring managers look for precision, avoidance of ambiguous jargon, and ability to explain trade-offs under real production conditions.
Comprehensive Model Answer
Verified Solution
Dimensions change over time: a customer moves city, a product changes category. A slowly changing dimension strategy decides how history is preserved.
- Type 1: overwrite the old value. Simple, but history is lost.
- Type 2: add a new row per change with valid_from and valid_to dates plus a current flag. This preserves full history and supports point-in-time reporting.
- Type 3: keep a previous_value column, limited to one prior state.
- Type 4 and 6: hybrids using history tables or current plus historical rows.
Type 2 is the most common for analytics. A fact row stores the dimension surrogate key valid when the event occurred, so joining facts to the correct historical version is automatic.
SELECT * FROM dim_customer
WHERE customer_id = 42 AND is_current = TRUE;
The downside is that the dimension grows and merge logic is more complex.
Candidate Response Strategy & Interview Tips
- Start with a concise one-sentence summary: Deliver a direct, confident answer first before expanding into nuances.
- Demonstrate real-world trade-offs: Discuss where this approach excels and when you would avoid it in production systems.
- Discuss complexity & edge cases: Proactively explain time/space complexity or boundary conditions (null values, scale limits).
- Prepare for interviewer follow-ups: Technical hiring panels frequently probe deeper into concurrency, backward compatibility, or alternative libraries.