What are the differences between DataFrame and Dataset in Spark?

DataFrames in Spark are a type of distributed dataset organized in a tabular format, similar to tables in relational databases. They offer a diverse set of APIs for manipulating and transforming data.

The Dataset is a new data structure introduced in Spark, it is a type-safe collection that can store different types of data. The Dataset can be thought of as both a strongly-typed DataFrame and a distributed dataset.

Therefore, DataFrame is a data collection similar to a table, while Dataset is a more versatile and type-safe data collection. It is generally recommended to use Dataset instead of DataFrame in Spark, as Dataset offers better type safety and a richer API.