Summary
In this chapter, we learned about Spark’s architecture and its inner workings. This exploration of Spark’s distributed computing landscape covered different Spark components, such as the Spark driver and SparkSession. We also talked about the different types of cluster managers available in Spark. Then, we touched on different types of partitioning regarding Spark and its deployment modes.
Next, we discussed Spark executors, jobs, stages, and tasks and highlighted the differences between them before learning about RDDs and their transformation types, learning more about narrow and wide transformations.
These concepts form the foundation for harnessing Spark’s immense capabilities in distributed data processing and analytics.
In the next chapter, we will discuss Spark DataFrames and their corresponding operations.
