RDD (Resilient Distributed Dataset) is a fundamental data structure in PySpark; it is an immutable and distributed collection of elements that can be processed in parallel. Some common RDD operations include

• Transformations: These are operations that return a new RDD and are lazily evaluated. Examples include a map, filter, flatMap, groupByKey, reduceByKey, join, etc.
• Actions: These are operations that return a value or write data to an external storage system and trigger the execution of transformations. Examples include count, first, take, collect, reduce, for each, saveAsTextFile, countByKey, etc.
• Persistence: RDDs can be stored in memory or on disk for faster access. Standard persistence options include persist, cache, unpersist, etc.
• Partitioning: RDDs can be split into partitions for parallel processing. The number of partitions can be specified or changed using operations like repartition, merge, etc.
• Caching: RDDs can be cached in memory for faster access to frequently used data.
• It’s worth noting that RDDs are being replaced by DataFrames and Datasets, which provide a more efficient and easy-to-use API for working with structured data.