RDD :
* It is Spark's structural square. RDDs contain all datasets and dataframes.
* If a similar arrangement of data needs to be calculated again, RDDs can be efficiently reserved.
* It's useful when you need to do low-level transformations, operations, and control on a dataset.
* It's more commonly used to alter data with functional programming structures than with domain-specific expressions.
DataFrame :
* It allows the structure, i.e., lines and segments, to be seen. You can think of it as a database table.
* Optimized Execution Plan- The catalyst analyzer is used to create query plans.
* One of the limitations of dataframes is Compile Time Wellbeing, i.e., when the structure of information is unknown, no control of information is possible.
* Also, if you're working on Python, start with DataFrames and then switch to RDDs if you need more flexibility.
DataSet (A subset of DataFrames) :
* It has the best encoding component and, unlike information edges, it enables time security in an organized manner.
* If you want a greater level of type safety at compile-time, or if you want typed JVM objects, Dataset is the way to go.
* Also, you can leverage datasets in situations where you are looking for a chance to take advantage of Catalyst optimization or even when you are trying to benefit from Tungsten’s fast code generation.
