In PySpark, RDD (Resilient Distributed Datasets) and DataFrame are two fundamental data structures for working with distributed data processing.
The main difference between RDDs and DataFrames is the level of abstraction they provide. RDDs offer low-level control over distributed data processing, while DataFrames provide a higher-level abstraction that simplifies common data manipulation tasks. DataFrames also provide optimizations such as query optimization and caching, which can make them faster than RDDs in certain scenarios.
In summary, RDDs are more suitable for low-level, complex data processing tasks, while DataFrames are more suitable for higher-level data analysis and manipulation tasks that involve structured data.
If you are interested in learning more about it, then don’t miss checking out the below video tutorial on PySpark -