A Directed Acyclic Graph (DAG) represents a workflow structure where tasks are executed in a defined order without forming cycles. It is widely used in distributed computing systems to manage task dependencies and optimize execution planning. This training explains core concepts such as nodes, edges, dependencies, and execution paths in a DAG structure. It also covers how DAGs are used in big data frameworks to break jobs into stages, schedule tasks, and improve parallel processing efficiency. You will learn how systems like Apache Spark use DAG-based execution to optimize performance and resource utilization. The course also highlights best practices for designing efficient workflows and managing complex data processing pipelines.
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