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Home/Questions/Spark/Big Data/Write a PySpark script to check for missing values and duplicate rows in a DataFrame. How would you ensure data quality before saving it to a storage system?

Write a PySpark script to check for missing values and duplicate rows in a DataFrame. How would you ensure data quality before saving it to a storage system?

Spark/Big Datahard0.9 min readPremium
Frequency
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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
Dunnhumby
Key Concepts Tested
partitionspark
Expert AnswerPremium
172 wordsInterview-ready
**Why It Matters (Architectural Logic)**: Data quality gates prevent downstream corruption, wasted compute, and compliance issues. Systematic validation balances rigor with performance. Data quality validation in PySpark requires systematic checks before persistence. For missing values, iterate over columns and use count() with filter(isnull()): `for col in df.columns: null_count = df.filter(F.col(col).isNull()).count()`; log or raise if thresholds exceeded....
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