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Most Spark beginners handle nulls wrong. Here’s what’s actually happening inside your DataFrame.
Picture this: You’ve built a beautiful PySpark pipeline. Data flows in, transformations run clean, everything looks perfect. Then your manager asks for the average salary across employees — and you get back a number that makes no sense.
Why? Because 30% of your salary column was null
, and you never handled it.
Null values are the silent killers of data pipelines. They don’t throw errors. They don’t scream for attention. They just quietly corrupt your aggregations, break your ML models, and make your dashboards lie to you.
This article is about the PySpark null handling interview question that shows up in almost every data engineering round — and more importantly, the deep understanding behind it that separates junior engineers from senior ones.
🎯 Why Null Handling Is a Big Deal in Real Pipelines #
Here’s the problem nobody talks about in tutorials: null values are everywhere in real-world data.
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A user never filled in their salary on a form →
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A sensor failed to record a reading →
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