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Explain SCD1 and SCD2 in Databricks PySpark with examples.

Spark/Big Datahard0.6 min readPremium
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
Expert AnswerPremium
114 wordsInterview-ready
**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))....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Hexaware. The answer also includes follow-up discussion points that interviewers commonly explore.

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