Answer / Mudit Kumar

In Apache Spark, both DataFrame and Dataset are high-level abstractions for structured data. The main difference lies in their type safety. A DataFrame is a distributed collection of data organized into named columns. It allows schema inference at runtime, making it dynamic but potentially less safe due to possible type errors. On the other hand, a Dataset is a strong typed, immutable distributed collection of data that provides the benefits of both RDDs (Resilient Distributed Datasets) and DataFrames: it can be processed using functional transformations just like an RDD while still providing optimized execution for SQL operations. However, it requires a known schema at compile time, ensuring type safety.

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