Spark DataFrame vs Dataset: Key Differences

Both DataFrame and Dataset are data structures used in Spark to represent datasets, but they have some differences in Spark.

1. A DataFrame is a distributed dataset organized in a tabular format similar to a relational database table, where each row represents a record and each column represents a field. DataFrames are a higher-level abstraction based on RDDs, providing many convenient operation functions and optimized execution plans.
2. The Dataset is a new data abstraction introduced in Spark version 1.6. It is strongly typed, enabling compile-time type checks and code optimizations. Combining the features of both DataFrames and RDDs, Datasets allow for flexibility in data manipulation through programming interfaces and high-performance optimization through SQL queries.
3. In Spark, a DataFrame is a special type of Dataset where its elements are of type Row, while a Dataset can be any Java object.
4. DataFrame is better suited for handling structured data, while Dataset is better suited for handling semi-structured or unstructured data. DataFrame provides more built-in functions and operations for easily processing data, while Dataset allows for more flexibility in defining and handling data.

In general, both DataFrame and Dataset are advanced abstractions for processing data, but DataFrame is more suitable for structured data, while Dataset is better for processing semi-structured and unstructured data. DataFrame is a specific form of Dataset, and in most cases, DataFrame can be used to complete data processing tasks.