What is the difference between DataFrame and Dataset in Spark?

In Spark, both DataFrame and Dataset are data structures used to represent data, but there are some differences.

1. A DataFrame is a data structure that organizes data in a tabular form, similar to a table in a relational database. It is a weakly-typed data structure, meaning it does not check types at compile time but rather at runtime. DataFrames provide a range of functions such as filtering, sorting, aggregation, etc., making it easy to manipulate data.
2. Introduced in Spark 2.0, Dataset is a new data structure that is strongly-typed, meaning it checks types at compile time. It can be converted to a DataFrame and manipulated through programming interfaces. In some cases, Dataset has better performance as it can optimize code using compile-time type information.

In general, DataFrames are suitable for handling structured data, while Datasets are suitable for handling semi-structured data or scenarios that require stricter type checking. In practical applications, you can choose to use either DataFrames or Datasets based on the specific situation.