What are the differences between DataFrame and RDD in Spark?

In Spark, both DataFrame and RDD are abstract data types, but they have some differences in terms of usage and manipulation.

1. DataFrame is an advanced abstraction based on RDD, which offers a more advanced API and richer functionality. It is a column-centric data structure, resembling tables in relational databases, where each column has its own data type. DataFrame can be operated and queried using SQL queries, DataFrame API, and Spark SQL.
2. RDD (Resilient Distributed Dataset) is the most fundamental data abstraction in Spark, it is an immutable distributed collection of objects. RDD provides lower-level operation interfaces such as map, filter, reduce, etc., where users need to manually manage data partitioning and scheduling. On the other hand, DataFrame offers a higher-level abstraction, hiding the underlying partitioning and scheduling details, making it more convenient for users to perform data processing and analysis.

In general, DataFrames are considered more advanced and convenient compared to RDDs, making them suitable for data processing and analysis. On the other hand, RDDs are more flexible and better suited for situations requiring customized data processing logic. It is recommended to choose between DataFrame and RDD based on specific requirements in practical use.