Explain difference between dataset and dataframe

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A dataset is a collection of data that can be processed in parallel, while a dataframe is a distributed collection of data organized into named columns.

A dataset is an abstraction of a distributed collection of data, while a dataframe is a distributed collection of data organized into named columns.
A dataset can be created from a variety of data sources, such as structured data files, tables in Hive, external databases, or existing RDDs, while a dataframe is a distributed collection of data organized into named columns.
A dataset provides a type-safe, object-oriented programming interface, while a dataframe provides a programming interface to manipulate structured and semi-structured data.
A dataset supports both functional and relational operations, while a dataframe supports relational operations on both external data sources and Spark's built-in distributed collections.
A dataset can be converted to a dataframe by calling the `.toDF()` method, while a dataframe can be converted to a dataset by calling the `.as()` method and providing a case class or a Java bean class.

Explain difference between dataset and dataframe

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