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Remove duplicates, fill missing values, and apply schema validation using ScalaSpark

SQLeasy0.3 min readPremium

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54 wordsInterview-ready

Scala Spark: df.dropDuplicates().na.fill(0).filter(col("amount").isNotNull). For schema: df.filter(col("col").cast("int").isNotNull) or use schemaOf and validate. With Dataset: ds.dropDuplicates("id").na.fill(Map("col" -> 0)). Schema validation: expectStructType or try/catch on read. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead. **Scalability trade-offs**: Profile before optimizing; validate on sample then full....

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