Hadoop: Execution Models

This lecture covers the execution models for distributed computing, focusing on Hadoop and MapReduce. It explains the concepts of primary and backup copies, interleaved declustering, and failure management in distributed systems. The instructor discusses the challenges of fault tolerance, data locality, and scheduling in Hadoop, as well as the architectural choices and optimizations in the Hadoop ecosystem. The lecture also delves into the limitations of MapReduce, the importance of data safety, fault tolerance mechanisms, and alternative distributed processing frameworks like Spark and Pregel.