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What is the difference between Lazy Evaluation and Eager Execution in PySpark?

Spark/Big Datahard0.4 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
Incedo
Key Concepts Tested
joinoptimizationsparksql
Expert AnswerPremium
86 wordsInterview-ready
**Lazy**: Transformations build DAG; nothing runs until action. Catalyst optimizes full plan. PySpark default. **Eager**: Each op runs immediately. Pandas, traditional SQL. No cross-op optimization. **Why Lazy**: Catalyst needs full plan for predicate pushdown, join reorder, constant folding. Single execution of optimized plan. **Trade-offs**: Lazy = optimization, single execution. Eager = immediate feedback, no optimization. **Scalability Trade-offs**: Long lineage = scheduler overhead....
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 Incedo. The answer also includes follow-up discussion points that interviewers commonly explore.

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