In this chapter, we introduced the readers to Apache Spark compute engine, and we talked about the various components of Spark framework and its schedulers. We touched upon the advantages that provide edge to spark as a scalable, high performing compute engine. The users were also acquainted with the not-so sparkling features of Spark—when it's not the best fit to be used. We walked the users through some practical realtime industry wide use cases. Next we got into the layered architecture of Spark and its internal working across the cluster. In the end we touched upon the pragmatic concepts of Spark: RDD, DataFrame, and datasets.
The next chapter will touch upon the spark APIs and execution of all components through working code blocks.
