Representing the workflow of pipelines as a graph fundamentally relies on the idea of defining dependencies between different components. When the outputs of one component serve as the inputs to another component, it creates a directed connection or pathway from one point in the graph to another. This approach effectively illustrates the flow of data and the step-by-step execution order, making it clear how the output from one stage is crucial for the functioning of the subsequent stage.
This graph-like representation is particularly beneficial for visualizing complex data pipelines, enabling easier identification of bottlenecks, dependencies, and potential areas for optimization, as well as facilitating troubleshooting and performance enhancements. By using the outputs from one component as inputs for others, developers can model intricate workflows inherently and logically.
The other approaches mentioned do not effectively illustrate the sequential or parallel dependencies that define workflow graphs. For instance, having all components defined in a single function might streamline implementation but does not visually represent the interaction between components. Running all components in parallel may enhance processing speed but complicates understanding the necessary sequence or dependency among different operations. Lastly, considering the outputs of a component as the pipeline's overall output lacks the detailed connectivity needed to conceive a comprehensive workflow graph.