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Basic Spark commands – Create RDD, Load data, Filter

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
Pubmatic
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
85 wordsInterview-ready
**Why syntax + semantics matter**: RDD vs DataFrame choice affects optimization; load options impact parallelism. **Commands**: Create RDD: `sc.parallelize([1,2,3])` or `sc.textFile("path")`. Load: `textFile`, `wholeTextFiles`, `sequenceFile` (RDD); `spark.read.csv()`, `spark.read.parquet()` (DataFrame). Filter: `rdd.filter(lambda x: x > 0)`; `df.filter(col("status") == "active")`....
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 Pubmatic. The answer also includes follow-up discussion points that interviewers commonly explore.

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