In the context of data orchestration tools like Dagster and Airflow, the terms "imperative" and "declarative" refer to different paradigms for defining and managing data pipelines. An imperative approach, which is more aligned with how Airflow operates, involves giving explicit commands to the system on what to do and in what order. In Airflow, you define Directed Acyclic Graphs (DAGs) that specify the sequence of tasks to be executed. Each task in the DAG represents a command or a piece of code that needs to be run. The focus is on the steps required to achieve the end result, and the user is responsible for managing the state and order of execution. On the other hand, a declarative approach, which Dagster emphasizes, involves describing the desired end state of the system and letting the orchestration tool manage the changes needed to achieve that state. In Dagster, you define data pipelines in terms of the data assets they produce and consume. The focus is on the assets themselves, and the relationships between them, rather than the specific commands or code that need to be run. Dagster manages the execution plan based on the defined assets and their dependencies, making it easier to understand, debug, and evolve data pipelines. The documentation highlights that declarative approaches are appealing because they make systems more debuggable and comprehensible by making intentions explicit. For example, instead of specifying a series of transformations, you would declare that "This table should exist, and should be derived from this file." Dagster's design principles center around data assets, encouraging a declarative mindset where you think in terms of the assets and their relationships. In summary, the key difference in the context of Dagster vs. Airflow is that Airflow takes an imperative approach where the user defines the exact sequence of tasks, while Dagster takes a declarative approach where the user defines the desired data assets and their relationships, and Dagster manages the execution accordingly.
