To ensure data loading activities are executed in sequence, what should be created?

Creating a pipeline with dependencies is essential for ensuring that data loading activities are executed in a specific sequence. This means that one task can only commence once the previous task has successfully completed. By establishing dependencies within a pipeline, you control the execution flow, which is particularly crucial in scenarios where the output of one process serves as the input for another. This sequential approach prevents errors that may arise from concurrently executing tasks that require ordered operations, enhancing data integrity and workflow efficiency.

In contrast, a scheduled job generally manages timing rather than the sequence of task execution. A dataflow typically focuses on the movement or transformation of data without inherently addressing execution order. A Spark job definition historically corresponds to executing distributed computations and, while it might support some level of task orchestration, it does not inherently manage dependencies between different data loading tasks in the same way a dedicated pipeline does.