**Why the distinction matters**: Cache = ephemeral compute-layer optimization; persistent storage = durable output. **cache()/persist()**: Stores RDD/DataFrame in memory (or disk) for reuse *within* job/DAG. Levels: MEMORY_ONLY, MEMORY_AND_DISK, etc. Evicted on LRU when full. **Persistent storage (Parquet, Delta)**: Durable, optimized format on disk/S3; survives cluster tear-down. **Scalability trade-offs**: Cache competes with execution memory; over-caching causes spill....
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 Snowflake. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
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.