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Read CSV, filter, and write to table using PySpark

Spark/Big Datahard0.3 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
61 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Read CSV, filter, write: `df = spark.read.option('header',True).csv('path'); filtered = df.filter(col('status')=='active'); filtered.write.mode('overwrite').saveAsTable('target_table')`. Or `.parquet('path')`....
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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