In this chapter, we first learned about the basic idea of an RDD. We then looked at how we can create RDDs using different approaches, such as creating an RDD from an existing RDD, from an external data store, from parallelizing a collection, and from a DataFrame and datasets. We also looked at the different types of transformations and actions available on RDDs. Then, the different types of RDDs were discussed, especially the pair RDD. We also discussed the benefits of caching and checkpointing in Spark applications, and then we learned about the partitions in more detail, and how we can make use of features like partitioning, to optimize our Spark jobs.
In the end, we also discussed some of the drawbacks of using RDDs. In the next chapter, we'll discuss the DataFrame and dataset APIs and see how they can overcome these challenges.
