Lecture
This lecture covers the Spark ecosystem, focusing on architectural choices such as the storage layer, programming models, fault tolerance, and RDDs. It discusses the limitations of MapReduce, the advantages of Spark's Resilient Distributed Datasets (RDDs), and the data flow model. The instructor explains how Spark improves expressiveness, extensibility, and performance by utilizing distributed RAM and enabling additional optimizations. Examples from Apache Spark, Microsoft, and Google are used to illustrate the concepts.