Como aprendi Apache Spark — Parte 9: Broadcast, Accumulators e Closures

Apache Spark architecture relies on a Driver to coordinate processing and Executors to run Tasks on data partitions. The system organizes work into Jobs, Stages, and Tasks, utilizing shuffle for data redistribution and lineage for fault tolerance. While traditional Python applications run in single processes, Spark distributes variables across Executors. Recent updates also distinguish between Client Mode and Cluster Mode, refining how applications are deployed and executed within modern environments to optimize large-scale data processing.