This document provides an overview of the Directed Acyclic Graph (DAG) used in this project for managing workflows in AWS.

A DAG is a collection of tasks organized in a way that defines their execution order based on dependencies. This ensures that tasks are executed in a sequence that respects their dependencies.

1. Nodes: Represent individual tasks in the workflow.
2. Edges: Define the dependencies between tasks.
3. Root Node: The starting point of the DAG.
4. Leaf Node: The final task in the sequence.

• Parallel Execution: Tasks without dependencies can run concurrently.
• Failure Handling: Easy to identify and handle failures in specific tasks.
• Scalability: Suits complex workflows with many interdependent tasks.

1. Define tasks in the tasks.py file.
2. Set up dependencies in the dag.py file.
3. Execute the DAG using the command: python run_dag.py.

For more details, refer to the AWS DAG documentation.