Job Schedulers in Python

For scheduling inside a single process, the schedule library offers a readable syntax for running functions every few minutes or at a set time, with no persistence. APScheduler is more complete: cron, interval and one-off triggers, persistent job stores so jobs survive restarts, and async support. Both run in your application, so they stop when it stops.

Workflow orchestrators manage many dependent tasks with retries, history and a UI. Apache Airflow defines pipelines as DAGs of tasks and is the most established choice for scheduled batch data work. Dagster models pipelines around the data assets they produce, with lineage and testing built in. Prefect turns ordinary Python functions into flows with decorators and needs less upfront infrastructure. Temporal is different: it provides durable execution, so a workflow can run for days and resume exactly where it left off after a crash. It suits long-running business processes and needs a Temporal server or Temporal Cloud.