Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Databricks caching: (1) .cache() or .persist()—stores DataFrame/RDD in executor memory; (2) Delta Cache—accelerates repeated reads from...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Incedo. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition) will help you answer variations of this question confidently.
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.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Databricks caching: (1) .cache() or .persist()—stores DataFrame/RDD in executor memory; (2) Delta Cache—accelerates repeated reads from cloud storage (S3/ADLS) by caching in SSD; (3) Predictive I/O—prefetches data. Use cache() for DataFrames reused across actions: df.cache(); df.filter(...).count(); df.filter(...).write. Delta Cache is automatic for frequently read Delta tables. Best practices: cache only when DataFrame is used multiple times; use StorageLevel.MEMORY_AND_DISK for large datasets; unpersist when done to free memory; avoid caching entire large tables unless necessary.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Caching without unpersist. Pro-Move: 'Cache reused 2+ times; MEMORY_AND_DISK for large.'
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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 an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.