Join me in this engaging and comprehensive episode of Think Tech that explores the powerful distributed computing system, Apache Spark, designed for big data processing. The episode covers the core concepts and inner workings of Spark, emphasizing its memory-centric architecture that enables lightning-fast processing and real-time or near-real-time capabilities. Listeners gain insights into Spark's fault-tolerant master/worker model, the significance of partitions for parallel processing, and the three essential data abstractions - RDD, Dataframe, and Dataset. The podcast also delves into Actions and Transformations, explaining their roles in optimizing data processing workflows. Additionally, the Spark Session as the entry point and the execution modes (Client, Cluster, and Local) for different scenarios are highlighted. Overall, the episode serves as an essential guide for understanding Apache Spark and its groundbreaking contributions to the world of big data processing.