What is the difference between DataFrame and Dataset in Apache Spark?

What is the difference between DataFrame and Dataset in Apache Spark?

DataFrames and Datasets are two key abstractions in Apache Spark that are commonly used for processing and manipulating data. Here are the main differences between the two:

• DataFrames: DataFrames are distributed collection of data organized into named columns. They are similar to tables in a relational database and offer a higher level of abstraction compared to RDDs (Resilient Distributed Datasets).
• Datasets: Datasets are a newer API that was introduced in Spark 2.0. They provide the benefits of DataFrames but also offer the type safety of RDDs. Datasets are strongly-typed, meaning that the data types of each column are known at compile time, which can help catch errors at an early stage.

In summary, while both DataFrames and Datasets are used for structured data processing in Apache Spark, Datasets offer the added benefit of type safety, making them a preferred choice for many developers.