Introduction to Spark runtime architecture

This lecture introduces Apache Spark, a unified analytics engine for large-scale data processing, highlighting its key features like interactive data exploration, in-memory data processing, and fault-tolerance. It covers the history of Spark, its usage in various applications, and its flexibility in deployment. The lecture explains Resilient Distributed Datasets (RDDs), the primary interface of Spark applications, and their importance in fault-tolerant and efficient iterative algorithms. It also delves into Spark's architecture, including the roles of the Driver and Worker nodes. Additionally, it explores RDD operations, transformations, actions, caching, and partitioning, providing insights into Spark's distributed computing framework.