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Explain caching techniques in Databricks.

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
Incedo
Key Concepts Tested
optimizationpartition
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. 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....
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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