Summary

This chapter proved that Flask[async] can work with different workflow engines, starting with Celery tasks. Flask[async], combined with the workflows created by Celery’s signatures and primitives, works well in building chained, grouped, and chorded processes.

Then, Flask[async] was proven to work with SpiffWorkflow for some BPMN serialization that focuses on UserTask and ScriptTask tasks. Also, this chapter even considered solving BPMN enterprise problems using the Zeebe/Camunda platform that showcases ServiceTask tasks.

Moreover, Flask[async] created an environment with Airflow 2.x to implement pipelines of tasks building an API orchestration. In the last part, the chapter established the integration between Flask[async] and Temporal.io and demonstrated the implementation of deterministic and distributed workflows.

This chapter provided a clear picture of the extensibility, usability, and scalability of the Flask framework in building scientific and big data...