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Home/Questions/Spark/Big Data/Explain SCD1 and SCD2 in Databricks PySpark with examples.

Explain SCD1 and SCD2 in Databricks PySpark with examples.

Spark/Big Datahard0.6 min readPremium

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. SCD1 (Slowly Changing Dimension Type 1) overwrites existing records with new data—no history. SCD2 maintains full history by adding new...

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Hexaware
Key Concepts Tested
joinoptimizationpartitionspark

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Hexaware. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, optimization, partition) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
114 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

SCD1 (Slowly Changing Dimension Type 1) overwrites existing records with new data—no history. SCD2 maintains full history by adding new version rows. SCD1 in PySpark: df_target.join(df_source, 'id', 'left').select(coalesce(df_source.col, df_target.col)). SCD2 in PySpark/Databricks: use merge with versioning—INSERT new rows, UPDATE current_flag=0 for changed rows, INSERT new version. Example SCD2: df.merge(target, 'id').whenMatchedUpdate(set={'current_flag': 0}).whenNotMatchedInsertAll(). Best practices: use Delta MERGE for SCD2; include effective_from, effective_to, current_flag columns; consider OPTIMIZE ZORDER BY for join performance on large SCD2 tables.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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